NIPS 2019 Proceedings

Advances in Neural Information Processing Systems 32 (NIPS 2019) pre-proceedings

The papers below appear in Advances in Neural Information Processing Systems 32 edited by H. Wallach and H. Larochelle and A. Beygelzimer and F. d'Alché-Buc and E. Fox and R. Garnett.
They are proceedings from the conference, "Neural Information Processing Systems 2019."

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers Liwei Wu, Shuqing Li, Cho-Jui Hsieh, James L. Sharpnack
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video Jiawang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid
Zero-shot Learning via Simultaneous Generating and Learning Hyeonwoo Yu, Beomhee Lee
Ask not what AI can do, but what AI should do: Towards a framework of task delegability Brian Lubars, Chenhao Tan
Stand-Alone Self-Attention in Vision Models Niki Parmar, Prajit Ramachandran, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jon Shlens
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee
Unsupervised learning of object structure and dynamics from videos Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin P. Murphy, Honglak Lee
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, zhifeng Chen
Meta-Learning with Implicit Gradients Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks Vineet Kosaraju, Amir Sadeghian, Roberto Martín-Martín, Ian Reid, Hamid Rezatofighi, Silvio Savarese
FreeAnchor: Learning to Match Anchors for Visual Object Detection Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, Qixiang Ye
Private Hypothesis Selection Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation Mark Bun, Thomas Steinke
Multi-Resolution Weak Supervision for Sequential Data Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, Christopher Ré
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection Vladimir V. Kniaz, Vladimir Knyaz, Fabio Remondino
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
Generalized Sliced Wasserstein Distances Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo Rohde
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise Thanh Huy Nguyen, Umut Simsekli, Mert Gurbuzbalaban, Gaël RICHARD
Blind Super-Resolution Kernel Estimation using an Internal-GAN Sefi Bell-Kligler, Assaf Shocher, Michal Irani
Noise-tolerant fair classification Alex Lamy, Ziyuan Zhong
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, Jun Zhou
Joint-task Self-supervised Learning for Temporal Correspondence Xueting Li, Sifei Liu, Shalini De Mello, Xiaolong Wang, Jan Kautz, Ming-Hsuan Yang
Provable Gradient Variance Guarantees for Black-Box Variational Inference Justin Domke
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke, Daniel R. Sheldon
Experience Replay for Continual Learning David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, Gregory Wayne
Deep ReLU Networks Have Surprisingly Few Activation Patterns Boris Hanin, David Rolnick
Chasing Ghosts: Instruction Following as Bayesian State Tracking Peter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, Stefan Lee
Block Coordinate Regularization by Denoising Yu Sun, Jiaming Liu, Ulugbek Kamilov
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova, Gauthier Gidel, François Fleuret, Simon Lacoste-Julien
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries Zihan Li, Matthias Fresacher, Jonathan Scarlett
A Primal-Dual link between GANs and Autoencoders Hisham Husain, Richard Nock, Robert C. Williamson
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation Qiming ZHANG, Jing Zhang, Wei Liu, Dacheng Tao
Invert to Learn to Invert Patrick Putzky, Max Welling
Equitable Stable Matchings in Quadratic Time Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras
Zero-Shot Semantic Segmentation Maxime Bucher, Tuan-Hung VU, Matthieu Cord, Patrick Pérez
Metric Learning for Adversarial Robustness Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, Baishakhi Ray
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, Ulrich Neumann
Batched Multi-armed Bandits Problem Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang
Differentially Private Bayesian Linear Regression Garrett Bernstein, Daniel R. Sheldon
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos Yitian Yuan, Lin Ma, Jingwen Wang, Wei Liu, Wenwu Zhu
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling Bichuan Guo, Yuxing Han, Jiangtao Wen
CPM-Nets: Cross Partial Multi-View Networks Changqing Zhang, Zongbo Han, yajie cui, Huazhu Fu, Joey Tianyi Zhou, Qinghua Hu
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, hongsheng Li
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling Andrey Kolobov, Yuval Peres, Cheng Lu, Eric J. Horvitz
SySCD: A System-Aware Parallel Coordinate Descent Algorithm Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell
Importance Weighted Hierarchical Variational Inference Artem Sobolev, Dmitry P. Vetrov
RSN: Randomized Subspace Newton Robert Gower, Dmitry Koralev, Felix Lieder, Peter Richtarik
Trust Region-Guided Proximal Policy Optimization Yuhui Wang, Hao He, Xiaoyang Tan, Yaozhong Gan
Adversarial Self-Defense for Cycle-Consistent GANs Dina Bashkirova, Ben Usman, Kate Saenko
Towards closing the gap between the theory and practice of SVRG Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis Bach, Robert Gower
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control Armin Lederer, Jonas Umlauft, Sandra Hirche
ETNet: Error Transition Network for Arbitrary Style Transfer Chunjin Song, Zhijie Wu, Yang Zhou, Minglun Gong, Hui Huang
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms Max Vladymyrov
Deep Equilibrium Models Shaojie Bai, J. Zico Kolter, Vladlen Koltun
Saccader: Improving Accuracy of Hard Attention Models for Vision Gamaleldin Elsayed, Simon Kornblith, Quoc V. Le
Multiway clustering via tensor block models Miaoyan Wang, Yuchen Zeng
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives Wang Chi Cheung
NAT: Neural Architecture Transformer for Accurate and Compact Architectures Yong Guo, Yin Zheng, Mingkui Tan, Qi Chen, Jian Chen, Peilin Zhao, Junzhou Huang
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression Ruidi Chen, Ioannis Paschalidis
Network Pruning via Transformable Architecture Search Xuanyi Dong, Yi Yang
Differentiable Cloth Simulation for Inverse Problems Junbang Liang, Ming Lin, Vladlen Koltun
Poisson-Randomized Gamma Dynamical Systems Aaron Schein, Scott Linderman, Mingyuan Zhou, David Blei, Hanna Wallach
Volumetric Correspondence Networks for Optical Flow Gengshan Yang, Deva Ramanan
Learning Conditional Deformable Templates with Convolutional Networks Adrian Dalca, Marianne Rakic, John Guttag, Mert Sabuncu
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu
Efficient Symmetric Norm Regression via Linear Sketching Zhao Song, Ruosong Wang, Lin Yang, Hongyang Zhang, Peilin Zhong
RUBi: Reducing Unimodal Biases for Visual Question Answering Remi Cadene, Corentin Dancette, Hedi Ben younes, Matthieu Cord, Devi Parikh
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition Jinwoo Choi, Chen Gao, Joseph C. E. Messou, Jia-Bin Huang
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma
DATA: Differentiable ArchiTecture Approximation Jianlong Chang, xinbang zhang, Yiwen Guo, GAOFENG MENG, SHIMING XIANG, Chunhong Pan
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
Memory-oriented Decoder for Light Field Salient Object Detection Miao Zhang, Jingjing Li, JI WEI, Yongri Piao, Huchuan Lu
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition Xuesong Niu, Hu Han, Shiguang Shan, Xilin Chen
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels Natalia Neverova, David Novotny, Andrea Vedaldi
Powerset Convolutional Neural Networks Chris Wendler, Markus Püschel, Dan Alistarh
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer Arsenii Vanunts, Alexey Drutsa
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums Hadrien Hendrikx, Francis Bach, Laurent Massoulié
Point-Voxel CNN for Efficient 3D Deep Learning Zhijian Liu, Haotian Tang, Yujun Lin, Song Han
Deep Learning without Weight Transport Mohamed Akrout, Collin Wilson, Peter Humphreys, Timothy Lillicrap, Douglas B. Tweed
Combinatorial Bandits with Relative Feedback Aadirupa Saha, Aditya Gopalan
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao
A Condition Number for Joint Optimization of Cycle-Consistent Networks Leonidas J. Guibas, Qixing Huang, Zhenxiao Liang
Explicit Disentanglement of Appearance and Perspective in Generative Models Nicki Skafte, Søren Hauberg
Polynomial Cost of Adaptation for X-Armed Bandits Hedi Hadiji
Learning to Propagate for Graph Meta-Learning LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
Secretary Ranking with Minimal Inversions Sepehr Assadi, Eric Balkanski, Renato Leme
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes Siqi Liu, Milos Hauskrecht
Learning Perceptual Inference by Contrasting Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, HongJing Lu, Song-Chun Zhu
Selecting the independent coordinates of manifolds with large aspect ratios Yu-Chia Chen, Marina Meila
Region-specific Diffeomorphic Metric Mapping Zhengyang Shen, Francois-Xavier Vialard, Marc Niethammer
Deep Supervised Summarization: Algorithm and Application to Learning Instructions Chengguang Xu, Ehsan Elhamifar
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations Vincent Sitzmann, Michael Zollhoefer, Gordon Wetzstein
Reconciling λ-Returns with Experience Replay Brett Daley, Christopher Amato
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence Fengxiang He, Tongliang Liu, Dacheng Tao
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs Max Simchowitz, Kevin G. Jamieson
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama
Combinatorial Inference against Label Noise Paul Hongsuck Seo, Geeho Kim, Bohyung Han
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong
Convolution with even-sized kernels and symmetric padding Shuang Wu, Guanrui Wang, Pei Tang, Feng Chen, Luping Shi
On The Classification-Distortion-Perception Tradeoff Dong Liu, Haochen Zhang, Zhiwei Xiong
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up Dominic Richards, Patrick Rebeschini
Online sampling from log-concave distributions Holden Lee, Oren Mangoubi, Nisheeth Vishnoi
Envy-Free Classification Maria-Florina F. Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
Finding Friend and Foe in Multi-Agent Games Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum
Image Synthesis with a Single (Robust) Classifier Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry
Model Compression with Adversarial Robustness: A Unified Optimization Framework Shupeng Gui, Haotao N. Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu
Cross-channel Communication Networks Jianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, Devi Parikh
CondConv: Conditionally Parameterized Convolutions for Efficient Inference Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam
Regression Planning Networks Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li F. Fei-Fei
Twin Auxilary Classifiers GAN Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich
Conditional Structure Generation through Graph Variational Generative Adversarial Nets Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, Pan Li
Distributional Policy Optimization: An Alternative Approach for Continuous Control Chen Tessler, Guy Tennenholtz, Shie Mannor
Sampling Sketches for Concave Sublinear Functions of Frequencies Edith Cohen, Ofir Geri
Deliberative Explanations: visualizing network insecurities Pei Wang, Nuno Nvasconcelos
Computing Full Conformal Prediction Set with Approximate Homotopy Eugene Ndiaye, Ichiro Takeuchi
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift Stephan Rabanser, Stephan Günnemann, Zachary Lipton
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang
Multi-View Reinforcement Learning Minne Li, Lisheng Wu, Jun WANG, Haitham Bou Ammar
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution Thang Vu, Hyunjun Jang, Trung X. Pham, Chang Yoo
Neural Diffusion Distance for Image Segmentation Jian Sun, Zongben Xu
Fine-grained Optimization of Deep Neural Networks Mete Ozay
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions Chris Russell, Matteo Toso, Neill Campbell
Hyperspherical Prototype Networks Pascal Mettes, Elise van der Pol, Cees Snoek
Expressive power of tensor-network factorizations for probabilistic modeling Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points Zhize Li
Efficient Meta Learning via Minibatch Proximal Update Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
Unconstrained Monotonic Neural Networks Antoine Wehenkel, Gilles Louppe
Guided Similarity Separation for Image Retrieval Chundi Liu, Guangwei Yu, Maksims Volkovs, Cheng Chang, Himanshu Rai, Junwei Ma, Satya Krishna Gorti
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma
Strategizing against No-regret Learners Yuan Deng, Jon Schneider, Balasubramanian Sivan
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
Hierarchical Optimal Transport for Document Representation Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes Rui Li
Positional Normalization Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
Quadratic Video Interpolation Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, Ming-Hsuan Yang
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies Bao Wang, Zuoqiang Shi, Stanley Osher
Incremental Scene Synthesis Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, YingLi Tian, Jan Ernst, ANDREAS HUTTER
Self-Supervised Generalisation with Meta Auxiliary Learning Shikun Liu, Andrew Davison, Edward Johns
Variational Denoising Network: Toward Blind Noise Modeling and Removal Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang
Fast Sparse Group Lasso Yasutoshi Ida, Yasuhiro Fujiwara, Hisashi Kashima
Learnable Tree Filter for Structure-preserving Feature Transform Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis Yuki Yoshida, Masato Okada
Coordinated hippocampal-entorhinal replay as structural inference Talfan Evans, Neil Burgess
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction Hao Zheng, Faming Fang, Guixu Zhang
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio, Leon Bottou
On the Curved Geometry of Accelerated Optimization Aaron Defazio
Multi-marginal Wasserstein GAN Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, Mingkui Tan
Better Exploration with Optimistic Actor Critic Kamil Ciosek, Quan Vuong, Robert Loftin, Katja Hofmann
Importance Resampling for Off-policy Prediction Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White
The Label Complexity of Active Learning from Observational Data Songbai Yan, Kamalika Chaudhuri, Tara Javidi
Meta-Learning Representations for Continual Learning Khurram Javed, Martha White
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang, Jianyu Wang
Visualizing the PHATE of Neural Networks Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers Alex Lu, Amy Lu, Wiebke Schormann, Marzyeh Ghassemi, David Andrews, Alan Moses
Nonconvex Low-Rank Tensor Completion from Noisy Data Changxiao Cai, Gen Li, H. Vincent Poor, Yuxin Chen
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman
Channel Gating Neural Networks Weizhe Hua, Yuan Zhou, Christopher M. De Sa, Zhiru Zhang, G. Edward Suh
Neural networks grown and self-organized by noise Guruprasad Raghavan, Matt Thomson
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, Jianmin Wang
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng
Variational Structured Semantic Inference for Diverse Image Captioning Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang
Mapping State Space using Landmarks for Universal Goal Reaching Zhiao Huang, Fangchen Liu, Hao Su
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan
Random deep neural networks are biased towards simple functions Giacomo De Palma, Bobak Kiani, Seth Lloyd
XNAS: Neural Architecture Search with Expert Advice Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik
CNN^{2}: Viewpoint Generalization via a Binocular Vision Wei-Da Chen, Shan-Hung (Brandon) Wu
Generalized Off-Policy Actor-Critic Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
DAC: The Double Actor-Critic Architecture for Learning Options Shangtong Zhang, Shimon Whiteson
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models Tao Yu, Christopher M. De Sa
Controlling Neural Level Sets Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
Blended Matching Pursuit Cyrille Combettes, Sebastian Pokutta
An Improved Analysis of Training Over-parameterized Deep Neural Networks Difan Zou, Quanquan Gu
Controllable Text-to-Image Generation Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip Torr
Improving Textual Network Learning with Variational Homophilic Embeddings Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach Peilin Zhong, Yuchen Mo, Chang Xiao, Pengyu Chen, Changxi Zheng
The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen, Yin Tat Lee
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update Su Young Lee, Choi Sungik, Sae-Young Chung
Fully Neural Network based Model for General Temporal Point Processes Takahiro Omi, naonori ueda, Kazuyuki Aihara
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, Ping Wang
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design Faidra Georgia Monachou, Itai Ashlagi
Provably Powerful Graph Networks Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman
Order Optimal One-Shot Distributed Learning Arsalan Sharifnassab, Saber Salehkaleybar, S. Jamaloddin Golestani
Information Competing Process for Learning Diversified Representations Jie Hu, Rongrong Ji, ShengChuan Zhang, Xiaoshuai Sun, Qixiang Ye, Chia-Wen Lin, Qi Tian
GENO -- GENeric Optimization for Classical Machine Learning Soeren Laue, Matthias Mitterreiter, Joachim Giesen
Conditional Independence Testing using Generative Adversarial Networks Alexis Bellot, Mihaela van der Schaar
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function Aviv Rosenberg, Yishay Mansour
Partitioning Structure Learning for Segmented Linear Regression Trees Xiangyu Zheng, Song Xi Chen
A Tensorized Transformer for Language Modeling Xindian Ma, Peng Zhang, Shuai Zhang, Nan Duan, Yuexian Hou, Ming Zhou, Dawei Song
Kernel Stein Tests for Multiple Model Comparison Jen Ning Lim, Makoto Yamada, Bernhard Schölkopf, Wittawat Jitkrittum
Disentangled behavioural representations Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David Cox
Rethinking the CSC Model for Natural Images Dror Simon, Michael Elad
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning Weishi Shi, Qi Yu
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros
Perceiving the arrow of time in autoregressive motion Kristof Meding, Dominik Janzing, Bernhard Schölkopf, Felix A. Wichmann
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li
Hyper-Graph-Network Decoders for Block Codes Eliya Nachmani, Lior Wolf
Large Scale Markov Decision Processes with Changing Rewards Adrian Rivera Cardoso, He Wang, Huan Xu
Multiview Aggregation for Learning Category-Specific Shape Reconstruction Srinath Sridhar, Davis Rempe, Julien Valentin, Bouaziz Sofien, Leonidas J. Guibas
Semi-Parametric Dynamic Contextual Pricing Virag Shah, Ramesh Johari, Jose Blanchet
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time Alan Kuhnle
Initialization of ReLUs for Dynamical Isometry Rebekka Burkholz, Alina Dubatovka
Gradient Information for Representation and Modeling Jie Ding, Robert Calderbank, Vahid Tarokh
SpiderBoost and Momentum: Faster Variance Reduction Algorithms Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases Xiyang Liu, Sewoong Oh
Backprop with Approximate Activations for Memory-efficient Network Training Ayan Chakrabarti, Benjamin Moseley
Training Image Estimators without Image Ground Truth Zhihao Xia, Ayan Chakrabarti
Deep Structured Prediction for Facial Landmark Detection Lisha Chen, Hui Su, Qiang Ji
Information-Theoretic Confidence Bounds for Reinforcement Learning Xiuyuan Lu, Benjamin Van Roy
Transfer Anomaly Detection by Inferring Latent Domain Representations Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
Total Least Squares Regression in Input Sparsity Time Huaian Diao, Zhao Song, David Woodruff, Xin Yang
Park: An Open Platform for Learning-Augmented Computer Systems Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, ravichandra addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Dr.Mohammad Alizadeh
Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi, David Blei, Victor Veitch
Learning Transferable Graph Exploration Hanjun Dai, Yujia Li, Chenglong Wang, Rishabh Singh, Po-Sen Huang, Pushmeet Kohli
Conformal Prediction Under Covariate Shift Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel Candes, Aaditya Ramdas
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation Chen Dan, Hong Wang, Hongyang Zhang, Yuchen Zhou, Pradeep K. Ravikumar
Asymmetric Valleys: Beyond Sharp and Flat Local Minima Haowei He, Gao Huang, Yang Yuan
Positive-Unlabeled Compression on the Cloud Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing XU, Dacheng Tao, Chang Xu
Direct Estimation of Differential Functional Graphical Models Boxin Zhao, Y. Samuel Wang, Mladen Kolar
On the Calibration of Multiclass Classification with Rejection Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller Pratyusha Sharma, Deepak Pathak, Abhinav Gupta
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Multi-mapping Image-to-Image Translation via Learning Disentanglement Xiaoming Yu, Yuanqi Chen, Shan Liu, Thomas Li, Ge Li
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Approximate Inference Turns Deep Networks into Gaussian Processes Mohammad Emtiyaz E. Khan, Alexander Immer, Ehsan Abedi, Maciej Korzepa
Deep Signature Transforms Patrick Kidger, Patric Bonnier, Imanol Perez Arribas, Cristopher Salvi, Terry Lyons
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits Yogev Bar-On, Yishay Mansour
Convergent Policy Optimization for Safe Reinforcement Learning Ming Yu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang
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Ultrametric Fitting by Gradient Descent Giovanni Chierchia, Benjamin Perret
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Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks Gauthier Gidel, Francis Bach, Simon Lacoste-Julien
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Optimal Decision Tree with Noisy Outcomes Su Jia, viswanath nagarajan, Fatemeh Navidi, R Ravi
Meta-Curvature Eunbyung Park, Junier B. Oliva
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The Landscape of Non-convex Empirical Risk with Degenerate Population Risk Shuang Li, Gongguo Tang, Michael B. Wakin
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Deep Generalized Method of Moments for Instrumental Variable Analysis Andrew Bennett, Nathan Kallus, Tobias Schnabel
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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask Hattie Zhou, Janice Lan, Rosanne Liu, Jason Yosinski
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Predicting the Politics of an Image Using Webly Supervised Data Christopher Thomas, Adriana Kovashka
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Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes Gunpil Hwang, Seohyeon Kim, Hyeon-Min Bae
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data Dominik Linzner, Michael Schmidt, Heinz Koeppl
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation Devin Reich, Ariel Todoki, Rafael Dowsley, Martine De Cock, anderson nascimento
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy Jonathan Ullman, Adam Sealfon
Learning Representations for Time Series Clustering Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell
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A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks Yiwen Guo, Ziang Yan, Changshui Zhang
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction Difan Zou, Pan Xu, Quanquan Gu
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
Cross-sectional Learning of Extremal Dependence among Financial Assets Xing Yan, Qi Wu, Wen Zhang
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Yujia Jin, Aaron Sidford
Compression with Flows via Local Bits-Back Coding Jonathan Ho, Evan Lohn, Pieter Abbeel
Exact Rate-Distortion in Autoencoders via Echo Noise Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan Yao
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction Aleksis Pirinen, Erik Gärtner, Cristian Sminchisescu
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization Shangyu Chen, Wenya Wang, Sinno Jialin Pan
Improved Precision and Recall Metric for Assessing Generative Models Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression JIAJIN LI, SEN HUANG, Anthony Man-Cho So
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph Yikang LI, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning ravichandra addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, Mohammad Alizadeh
Uncoupled Regression from Pairwise Comparison Data Liyuan Xu, Junya Honda, Gang Niu, Masashi Sugiyama
Cross Attention Network for Few-shot Classification Ruibing Hou, Hong Chang, Bingpeng MA, Shiguang Shan, Xilin Chen
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution Qing Qu, Xiao Li, Zhihui Zhu
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models Linfeng Zhang, Zhanhong Tan, Jiebo Song, Jingwei Chen, Chenglong Bao, Kaisheng Ma
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Teaching Multiple Concepts to a Forgetful Learner Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla
Regularized Weighted Low Rank Approximation Frank Ban, David Woodruff, Richard Zhang
Practical and Consistent Estimation of f-Divergences Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
Approximation Ratios of Graph Neural Networks for Combinatorial Problems Ryoma Sato, Makoto Yamada, Hisashi Kashima
Thinning for Accelerating the Learning of Point Processes Tianbo Li, Yiping Ke
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models Maxim Kuznetsov, Daniil Polykovskiy, Dmitry P. Vetrov, Alex Zhebrak
Differentially Private Markov Chain Monte Carlo Mikko Heikkilä, Joonas Jälkö, Onur Dikmen, Antti Honkela
Full-Gradient Representation for Neural Network Visualization Suraj Srinivas, François Fleuret
q-means: A quantum algorithm for unsupervised machine learning Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla
Limitations of the empirical Fisher approximation for natural gradient descent Frederik Kunstner, Philipp Hennig, Lukas Balles
Flow-based Image-to-Image Translation with Feature Disentanglement Ruho Kondo, Keisuke Kawano, Satoshi Koide, Takuro Kutsuna
Learning dynamic polynomial proofs Alhussein Fawzi, Mateusz Malinowski, Hamza Fawzi, Omar Fawzi
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models Vincent LE GUEN, Nicolas THOME
Understanding Attention and Generalization in Graph Neural Networks Boris Knyazev, Graham W. Taylor, Mohamed Amer
Data Cleansing for Models Trained with SGD Satoshi Hara, Atsushi Nitanda, Takanori Maehara
Curvilinear Distance Metric Learning Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang
Embedding Symbolic Knowledge into Deep Networks Xie Yaqi, Ziwei Xu, Kuldeep S Meel, Mohan Kankanhalli, Harold Soh
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K. Duvenaud, Raquel Urtasun, Richard Zemel
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms Mahesh Chandra Mukkamala, Peter Ochs
Learning Deep Bilinear Transformation for Fine-grained Image Representation Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
Practical Deep Learning with Bayesian Principles Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz E. Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
Training Language GANs from Scratch Cyprien de Masson d'Autume, Shakir Mohamed, Mihaela Rosca, Jack Rae
Pseudo-Extended Markov chain Monte Carlo Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate James Jordon, Jinsung Yoon, Mihaela van der Schaar
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters Alberto Maria Metelli, Amarildo Likmeta, Marcello Restelli
On Adversarial Mixup Resynthesis Christopher Beckham, Sina Honari, Alex M. Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Chris Pal
A Geometric Perspective on Optimal Representations for Reinforcement Learning Marc Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks Joshua Lee, Prasanna Sattigeri, Gregory Wornell
Understanding and Improving Layer Normalization Jingjing Xu, Xu Sun, Zhiyuan Zhang, Guangxiang Zhao, Junyang Lin
Uncertainty-based Continual Learning with Adaptive Regularization Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, Dacheng Tao
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging Mathias Perslev, Michael Jensen, Sune Darkner, Poul Jørgen Jennum, Christian Igel
Massively scalable Sinkhorn distances via the Nyström method Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed
Double Quantization for Communication-Efficient Distributed Optimization Yue Yu, Jiaxiang Wu, Longbo Huang
Globally optimal score-based learning of directed acyclic graphs in high-dimensions Bryon Aragam, Arash Amini, Qing Zhou
Multi-relational Poincaré Graph Embeddings Ivana Balazevic, Carl Allen, Timothy Hospedales
No-Press Diplomacy: Modeling Multi-Agent Gameplay Philip Paquette, Yuchen Lu, SETON STEVEN BOCCO, Max Smith, Satya O.-G., Jonathan K. Kummerfeld, Joelle Pineau, Satinder Singh, Aaron C. Courville
State Aggregation Learning from Markov Transition Data Yaqi Duan, Tracy Ke, Mengdi Wang
Disentangling Influence: Using disentangled representations to audit model predictions Charles Marx, Richard Phillips, Sorelle Friedler, Carlos Scheidegger, Suresh Venkatasubramanian
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
Partially Encrypted Deep Learning using Functional Encryption Théo Ryffel, David Pointcheval, Francis Bach, Edouard Dufour-Sans, Romain Gay
Decentralized Cooperative Stochastic Bandits David Martínez-Rubio, Varun Kanade, Patrick Rebeschini
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem Gonzalo Mena, Jonathan Niles-Weed
Efficient Deep Approximation of GMMs Shirin Jalali, Carl Nuzman, Iraj Saniee
Learning low-dimensional state embeddings and metastable clusters from time series data Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations Xu Wang, Jingming He, Lin Ma
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes Creighton Heaukulani, Mark van der Wilk
Kernel Instrumental Variable Regression Rahul Singh, Maneesh Sahani, Arthur Gretton
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments Hugo Caselles-Dupré, Michael Garcia Ortiz, David Filliat
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods Supratik Paul, Vitaly Kurin, Shimon Whiteson
Offline Contextual Bayesian Optimization Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark Boyer, Egemen Kolemen
Making the Cut: A Bandit-based Approach to Tiered Interviewing Candice Schumann, Zhi Lang, Jeffrey Foster, John Dickerson
Unsupervised Scalable Representation Learning for Multivariate Time Series Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI Tao Tu, John Paisley, Stefan Haufe, Paul Sajda
End to end learning and optimization on graphs Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
Game Design for Eliciting Distinguishable Behavior Fan Yang, Liu Leqi, Yifan Wu, Zachary Lipton, Pradeep K. Ravikumar, Tom M. Mitchell, William W. Cohen
When does label smoothing help? Rafael Müller, Simon Kornblith, Geoffrey E. Hinton
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning Harsh Gupta, R. Srikant, Lei Ying
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks Lixin Fan, Kam Woh Ng, Chee Seng Chan
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference Cole Hurwitz, Kai Xu, Akash Srivastava, Alessio Buccino, Matthias Hennig
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David Woodruff
Distribution-Independent PAC Learning of Halfspaces with Massart Noise Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies Basri Ronen, David Jacobs, Yoni Kasten, Shira Kritchman
Adaptive Auxiliary Task Weighting for Reinforcement Learning Xingyu Lin, Harjatin Baweja, George Kantor, David Held
Blocking Bandits Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities Wei Qian, Yuqian Zhang, Yudong Chen
Prior-Free Dynamic Auctions with Low Regret Buyers Yuan Deng, Jon Schneider, Balasubramanian Sivan
On Single Source Robustness in Deep Fusion Models Taewan Kim, Joydeep Ghosh
Policy Evaluation with Latent Confounders via Optimal Balance Andrew Bennett, Nathan Kallus
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting Rajat Sen, Hsiang-Fu Yu, Inderjit S. Dhillon
Adaptive Cross-Modal Few-shot Learning Chen Xing, Negar Rostamzadeh, Boris Oreshkin, Pedro O. O. Pinheiro
Spectral Modification of Graphs for Improved Spectral Clustering Ioannis Koutis, Huong Le
Hyperbolic Graph Convolutional Neural Networks Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec
Cost Effective Active Search Shali Jiang, Roman Garnett, Benjamin Moseley
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs Jian QIAN, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi Jaakkola
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees Ruqi Zhang, Christopher M. De Sa
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N. Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar
Fair Algorithms for Clustering Suman Bera, Deeparnab Chakrabarty, Nicolas Flores, Maryam Negahbani
Learning Mean-Field Games Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul Whatmough
Deep imitation learning for molecular inverse problems Eric Jonas
Visual Concept-Metaconcept Learning Chi Han, Jiayuan Mao, Chuang Gan, Josh Tenenbaum, Jiajun Wu
Few-shot Video-to-Video Synthesis Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Bryan Catanzaro, Jan Kautz
Neural Similarity Learning Weiyang Liu, Zhen Liu, James M. Rehg, Le Song
Ordered Memory Yikang Shen, Shawn Tan, Arian Hosseini, Zhouhan Lin, Alessandro Sordoni, Aaron C. Courville
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A. Raffel
Multivariate Triangular Quantile Maps for Novelty Detection Jingjing Wang, Sun Sun, Yaoliang Yu
Fast Parallel Algorithms for Statistical Subset Selection Problems Sharon Qian, Yaron Singer
PHYRE: A New Benchmark for Physical Reasoning Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick
On the number of variables to use in principal component regression Ji Xu, Daniel J. Hsu
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery Jicong Fan, Lijun Ding, Yudong Chen, Madeleine Udell
Mutually Regressive Point Processes Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
Data-driven Estimation of Sinusoid Frequencies Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang
ANODEV2: A Coupled Neural ODE Framework Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros
Estimating Entropy of Distributions in Constant Space Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun
On the Utility of Learning about Humans for Human-AI Coordination Micah Carroll, Rohin Shah, Mark K. Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, Anca Dragan
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm
Learning in Generalized Linear Contextual Bandits with Stochastic Delays Zhengyuan Zhou, Renyuan Xu, Jose Blanchet
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions Gabriele Farina, Christian Kroer, Tuomas Sandholm
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, Xifeng Yan
On the Accuracy of Influence Functions for Measuring Group Effects Pang Wei W. Koh, Kai-Siang Ang, Hubert Teo, Percy S. Liang
Face Reconstruction from Voice using Generative Adversarial Networks Yandong Wen, Bhiksha Raj, Rita Singh
Incremental Few-Shot Learning with Attention Attractor Networks Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel
On Testing for Biases in Peer Review Ivan Stelmakh, Nihar Shah, Aarti Singh
Learning Disentangled Representation for Robust Person Re-identification Chanho Eom, Bumsub Ham
Balancing Efficiency and Fairness in On-Demand Ridesourcing Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series Yulia Rubanova, Tian Qi Chen, David K. Duvenaud
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann
Input Similarity from the Neural Network Perspective Guillaume Charpiat, Nicolas Girard, Loris Felardos, Yuliya Tarabalka
Adaptive Sequence Submodularity Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi
Weight Agnostic Neural Networks Adam Gaier, David Ha
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction Daniel Freeman, David Ha, Luke Metz
Reducing the variance in online optimization by transporting past gradients Sébastien Arnold, Pierre-Antoine Manzagol, Reza Babanezhad Harikandeh, Ioannis Mitliagkas, Nicolas Le Roux
Characterizing Bias in Classifiers using Generative Models Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor
Optimal Stochastic and Online Learning with Individual Iterates Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou
Policy Learning for Fairness in Ranking Ashudeep Singh, Thorsten Joachims
Off-Policy Evaluation via Off-Policy Classification Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine
Regularized Gradient Boosting Corinna Cortes, Mehryar Mohri, Dmitry Storcheus
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood
Markov Random Fields for Collaborative Filtering Harald Steck
A Step Toward Quantifying Independently Reproducible Machine Learning Research Edward Raff
Scalable Global Optimization via Local Bayesian Optimization David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, Matthias Poloczek
Time-series Generative Adversarial Networks Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar
Ouroboros: On Accelerating Training of Transformer-Based Language Models Qian Yang, Zhouyuan Huo, Wenlin Wang, Lawrence Carin
A Refined Margin Distribution Analysis for Forest Representation Learning Shen-Huan Lyu, Liang Yang, Zhi-Hua Zhou
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda
An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
Adaptive Influence Maximization with Myopic Feedback Binghui Peng, Wei Chen
Focused Quantization for Sparse CNNs Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu
Quantum Embedding of Knowledge for Reasoning Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam
Optimal Best Markovian Arm Identification with Fixed Confidence Vrettos Moulos
Limiting Extrapolation in Linear Approximate Value Iteration Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill
Invertible Convolutional Flow Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth
A Latent Variational Framework for Stochastic Optimization Philippe Casgrain
Topology-Preserving Deep Image Segmentation Xiaoling Hu, Fuxin Li, Dimitris Samaras, Chao Chen
Connective Cognition Network for Directional Visual Commonsense Reasoning Aming Wu, Linchao Zhu, Yahong Han, Yi Yang
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms Vikas Garg, Tamar Pichkhadze
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning Francisco Garcia, Philip S. Thomas
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu
Learning Disentangled Representations for Recommendation Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels Simon S. Du, Kangcheng Hou, Russ R. Salakhutdinov, Barnabas Poczos, Ruosong Wang, Keyulu Xu
In-Place Zero-Space Memory Protection for CNN Hui Guan, Lin Ning, Zhen Lin, Xipeng Shen, Huiyang Zhou, Seung-Hwan Lim
Acceleration via Symplectic Discretization of High-Resolution Differential Equations Bin Shi, Simon S. Du, Weijie Su, Michael I. Jordan
XLNet: Generalized Autoregressive Pretraining for Language Understanding Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R. Salakhutdinov, Quoc V. Le
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex Jianghong Shi, Eric Shea-Brown, Michael Buice
Variance Reduced Policy Evaluation with Smooth Function Approximation Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang
Learning GANs and Ensembles Using Discrepancy Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
Co-Generation with GANs using AIS based HMC Tiantian Fang, Alexander Schwing
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin
Abstract Reasoning with Distracting Features Kecheng Zheng, Zheng-Jun Zha, Wei Wei
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang
Adversarial Training and Robustness for Multiple Perturbations Florian Tramer, Dan Boneh
Doubly-Robust Lasso Bandit Gi-Soo Kim, Myunghee Cho Paik
DM2C: Deep Mixed-Modal Clustering Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
MaCow: Masked Convolutional Generative Flow Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy
Learning by Abstraction: The Neural State Machine Drew Hudson, Christopher D. Manning
Adaptive Gradient-Based Meta-Learning Methods Mikhail Khodak, Maria-Florina F. Balcan, Ameet S. Talwalkar
Equipping Experts/Bandits with Long-term Memory Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning Wenhao Yang, Xiang Li, Zhihua Zhang
Scalable inference of topic evolution via models for latent geometric structures Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft
Deep Active Learning with a Neural Architecture Search Yonatan Geifman, Ran El-Yaniv
Efficiently escaping saddle points on manifolds Christopher Criscitiello, Nicolas Boumal
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks Jiong Zhang, Hsiang-Fu Yu, Inderjit S. Dhillon
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters W. O. K. Asiri Suranga Wijesinghe, Qing Wang
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning Wonjae Kim, Yoonho Lee
Comparing Unsupervised Word Translation Methods Step by Step Mareike Hartmann, Yova Kementchedjhieva, Anders Søgaard
Learning from Bad Data via Generation Tianyu Guo, Chang Xu, Boxin Shi, Chao Xu, Dacheng Tao
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration Florian Scheidegger, Luca Benini, Costas Bekas, A. Cristiano I. Malossi
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection Yihe Dong, Samuel Hopkins, Jerry Li
Iterative Least Trimmed Squares for Mixed Linear Regression Yanyao Shen, Sujay Sanghavi
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces Yu Qi, Bin Liu, Yueming Wang, Gang Pan
Divergence-Augmented Policy Optimization Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang
Intrinsic dimension of data representations in deep neural networks Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
Towards a Zero-One Law for Column Subset Selection Zhao Song, David Woodruff, Peilin Zhong
Compositional De-Attention Networks Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning Jian Ni, Shanghang Zhang, Haiyong Xie
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang
Mining GOLD Samples for Conditional GANs Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin
Deep Model Transferability from Attribution Maps Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, Mingli Song
Fully Parameterized Quantile Function for Distributional Reinforcement Learning Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
Direct Optimization through \arg \max for Discrete Variational Auto-Encoder Guy Lorberbom, Tommi Jaakkola, Andreea Gane, Tamir Hazan
Distributional Reward Decomposition for Reinforcement Learning Zichuan Lin, Li Zhao, Derek Yang, Tao Qin, Tie-Yan Liu, Guangwen Yang
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang
Convergence Guarantees for Adaptive Bayesian Quadrature Methods Motonobu Kanagawa, Philipp Hennig
Progressive Augmentation of GANs Dan Zhang, Anna Khoreva
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization Ali Kavis, Kfir Y. Levy, Francis Bach, Volkan Cevher
Meta-Surrogate Benchmarking for Hyperparameter Optimization Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez
Learning to Perform Local Rewriting for Combinatorial Optimization Xinyun Chen, Yuandong Tian
Anti-efficient encoding in emergent communication Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni
Singleshot : a scalable Tucker tensor decomposition Abraham Traore, Maxime Berar, Alain Rakotomamonjy
Neural Machine Translation with Soft Prototype Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu
Reliable training and estimation of variance networks Nicki Skafte, Martin Jørgensen, Søren Hauberg
Copula Multi-label Learning Weiwei Liu
Bayesian Learning of Sum-Product Networks Martin Trapp, Robert Peharz, Hong Ge, Franz Pernkopf, Zoubin Ghahramani
Bayesian Batch Active Learning as Sparse Subset Approximation Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation zengfeng Huang, Ziyue Huang, Yilei WANG, Ke Yi
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks Xiaohan Ding, guiguang ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu
Variational Bayesian Decision-making for Continuous Utilities Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
Single-Model Uncertainties for Deep Learning Natasa Tagasovska, David Lopez-Paz
Is Deeper Better only when Shallow is Good? Eran Malach, Shai Shalev-Shwartz
Wasserstein Weisfeiler-Lehman Graph Kernels Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
Domain Generalization via Model-Agnostic Learning of Semantic Features Qi Dou, Daniel Coelho de Castro, Konstantinos Kamnitsas, Ben Glocker
Grid Saliency for Context Explanations of Semantic Segmentation Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer
First-order methods almost always avoid saddle points: The case of vanishing step-sizes Ioannis Panageas, Georgios Piliouras, Xiao Wang
Maximum Mean Discrepancy Gradient Flow Michael Arbel, Anna Korba, Adil SALIM, Arthur Gretton
Oblivious Sampling Algorithms for Private Data Analysis Sajin Sasy, Olga Ohrimenko
Semi-supervisedly Co-embedding Attributed Networks Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders Natasa Tagasovska, Damien Ackerer, Thibault Vatter
Nonstochastic Multiarmed Bandits with Unrestricted Delays Tobias Sommer Thune, Nicolò Cesa-Bianchi, Yevgeny Seldin
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
Code Generation as a Dual Task of Code Summarization Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin
Diffeomorphic Temporal Alignment Nets Ron A. Shapira Weber, Matan Eyal, Nicki Skafte, Oren Shriki, Oren Freifeld
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior Cheng-Chun Hsu, Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Yung-Yu Chuang
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai, Ohad Shamir
Efficient Pure Exploration in Adaptive Round model tianyuan jin, Jieming SHI, Xiaokui Xiao, Enhong Chen
Multi-objects Generation with Amortized Structural Regularization Taufik Xu, Chongxuan LI, Jun Zhu, Bo Zhang
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time Karlis Freivalds, Emīls Ozoliņš, Agris Šostaks
DetNAS: Backbone Search for Object Detection Yukang Chen, Tong Yang, Xiangyu Zhang, GAOFENG MENG, Xinyu Xiao, Jian Sun
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates Adil SALIM, Dmitry Koralev, Peter Richtarik
Fast AutoAugment Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
Interval timing in deep reinforcement learning agents Ben Deverett, Ryan Faulkner, Meire Fortunato, Gregory Wayne, Joel Z. Leibo
Graph-based Discriminators: Sample Complexity and Expressiveness Roi Livni, Yishay Mansour
Large Scale Structure of Neural Network Loss Landscapes Stanislav Fort, Stanislaw Jastrzebski
Learning Nonsymmetric Determinantal Point Processes Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene
Hypothesis Set Stability and Generalization Dylan J. Foster, Spencer Greenberg, Satyen Kale, Haipeng Luo, Mehryar Mohri, Karthik Sridharan
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni
Precision-Recall Balanced Topic Modelling Seppo Virtanen, Mark Girolami
Learning Sparse Distributions using Iterative Hard Thresholding Jacky Y. Zhang, Rajiv Khanna, Anastasios Kyrillidis, Oluwasanmi O. Koyejo
Discriminative Topic Modeling with Logistic LDA Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak, Lucas Theis
Quantum Wasserstein Generative Adversarial Networks Shouvanik Chakrabarti, Huang Yiming, Tongyang Li, Soheil Feizi, Xiaodi Wu
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion Joan Serrà, Santiago Pascual, Carlos Segura Perales
Hyperparameter Learning via Distributional Transfer Ho Chung Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic
Discriminator optimal transport Akinori Tanaka
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
Are Anchor Points Really Indispensable in Label-Noise Learning? Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, Masashi Sugiyama
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, Xu Sun
Differentiable Ranking and Sorting using Optimal Transport Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks Gaël Letarte, Pascal Germain, Benjamin Guedj, Francois Laviolette
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery Chenwei DING, Mingming Gong, Kun Zhang, Dacheng Tao
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem DongDong Ge, Haoyue Wang, Zikai Xiong, Yinyu Ye
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections Boris Muzellec, Marco Cuturi
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent Huizhuo Yuan, Xiangru Lian, Chris Junchi Li, Ji Liu, Wenqing Hu
On the convergence of single-call stochastic extra-gradient methods Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
Infra-slow brain dynamics as a marker for cognitive function and decline Shagun Ajmera Shyam Sunder Ajmera, Shreya Rajagopal, Razi Rehman, Devarajan Sridharan
Robust Principal Component Analysis with Adaptive Neighbors Rui Zhang, Hanghang Tong
High-Quality Self-Supervised Deep Image Denoising Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup Sebastian Goldt, Madhu Advani, Andrew M. Saxe, Florent Krzakala, Lenka Zdeborová
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, Xiaowei Zhou
Online Prediction of Switching Graph Labelings with Cluster Specialists Mark Herbster, James Robinson
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch, Joost van Amersfoort, Yarin Gal
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs Marek Petrik, Reazul Hasan Russel
Cross-lingual Language Model Pretraining Alexis CONNEAU, Guillaume Lample
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input Maxence Ernoult, Benjamin Scellier, Yoshua Bengio, Damien Querlioz, Julie Grollier
Universal Invariant and Equivariant Graph Neural Networks Nicolas Keriven, Gabriel Peyré
Are sample means in multi-armed bandits positively or negatively biased? Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
On the Correctness and Sample Complexity of Inverse Reinforcement Learning Abi Komanduru, Jean Honorio
VIREL: A Variational Inference Framework for Reinforcement Learning Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
First Order Motion Model for Image Animation Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe
Tensor Monte Carlo: Particle Methods for the GPU era Laurence Aitchison
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction Alban Laflaquière, Michael Garcia Ortiz
Learning from Label Proportions with Generative Adversarial Networks Jiabin Liu, Bo Wang, Zhiquan Qi, YingJie Tian, Yong Shi
Efficient and Thrifty Voting by Any Means Necessary Debmalya Mandal, Ariel D. Procaccia, Nisarg Shah, David Woodruff
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation Can Qin, Haoxuan You, Lichen Wang, C.-C. Jay Kuo, Yun Fu
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning Erwan Lecarpentier, Emmanuel Rachelson
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea
Toward a Characterization of Loss Functions for Distribution Learning Nika Haghtalab, Cameron Musco, Bo Waggoner
Coresets for Archetypal Analysis Sebastian Mair, Ulf Brefeld
Emergence of Object Segmentation in Perturbed Generative Models Adam Bielski, Paolo Favaro
Optimal Sparse Decision Trees Xiyang Hu, Cynthia Rudin, Margo Seltzer
Escaping from saddle points on Riemannian manifolds Yue Sun, Nicolas Flammarion, Maryam Fazel
Multi-source Domain Adaptation for Semantic Segmentation Sicheng Zhao, Bo Li, Xiangyu Yue, Yang Gu, Pengfei Xu, Runbo Hu, Hua Chai, Kurt Keutzer
Localized Structured Prediction Carlo Ciliberto, Francis Bach, Alessandro Rudi
Nonzero-sum Adversarial Hypothesis Testing Games Sarath Yasodharan, Patrick Loiseau
Manifold-regression to predict from MEG/EEG brain signals without source modeling David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann
Modeling Tabular data using Conditional GAN Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Normalization Helps Training of Quantized LSTM Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, Tie-Yan Liu
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration Clarice Poon, Jingwei Liang
Deep Scale-spaces: Equivariance Over Scale Daniel Worrall, Max Welling
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau
Estimating Convergence of Markov chains with L-Lag Couplings Niloy Biswas, Pierre E. Jacob, Paul Vanetti
Learning-Based Low-Rank Approximations Piotr Indyk, Ali Vakilian, Yang Yuan
Implicit Regularization in Deep Matrix Factorization Sanjeev Arora, Nadav Cohen, Wei Hu, Yuping Luo
List-decodable Linear Regression Sushrut Karmalkar, Adam Klivans, Pravesh Kothari
Learning elementary structures for 3D shape generation and matching Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir Kim, Bryan Russell, Mathieu Aubry
On the Hardness of Robust Classification Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell
Foundations of Comparison-Based Hierarchical Clustering Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
What the Vec? Towards Probabilistically Grounded Embeddings Carl Allen, Ivana Balazevic, Timothy Hospedales
Minimizers of the Empirical Risk and Risk Monotonicity Marco Loog, Tom Viering, Alexander Mey
Explicit Planning for Efficient Exploration in Reinforcement Learning Liangpeng Zhang, Ke Tang, Xin Yao
Lower Bounds on Adversarial Robustness from Optimal Transport Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
Neural Spline Flows Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints David Simchi-Levi, Yunzong Xu
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder
Nonlinear scaling of resource allocation in sensory bottlenecks Laura Rose Edmondson, Alejandro Jimenez Rodriguez, Hannes P. Saal
Constrained Reinforcement Learning Has Zero Duality Gap Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Niklas Gebauer, Michael Gastegger, Kristof Schütt
An adaptive nearest neighbor rule for classification Akshay Balsubramani, Sanjoy Dasgupta, yoav Freund, Shay Moran
Coresets for Clustering with Fairness Constraints Lingxiao Huang, Shaofeng Jiang, Nisheeth Vishnoi
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments Ben Graham, David Novotny, Jeremy Reizenstein
MAVEN: Multi-Agent Variational Exploration Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
Competitive Gradient Descent Florian Schaefer, Anima Anandkumar
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Continual Unsupervised Representation Learning Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell
Self-Routing Capsule Networks Taeyoung Hahn, Myeongjang Pyeon, Gunhee Kim
The Parameterized Complexity of Cascading Portfolio Scheduling Eduard Eiben, Robert Ganian, Iyad Kanj, Stefan Szeider
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards Falcon Dai, Matthew Walter
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes Rishidev Chaudhuri, Ila Fiete
Sequence Modeling with Unconstrained Generation Order Dmitrii Emelianenko, Elena Voita, Pavel Serdyukov
Probabilistic Logic Neural Networks for Reasoning Meng Qu, Jian Tang
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant
A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening Gecia Bravo Hermsdorff, Lee Gunderson
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond Xuechen Li, Yi Wu, Lester Mackey
The Implicit Bias of AdaGrad on Separable Data Qian Qian, Xiaoyuan Qian
On two ways to use determinantal point processes for Monte Carlo integration Guillaume Gautier, Rémi Bardenet, Michal Valko
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition Zuxuan Wu, Caiming Xiong, Yu-Gang Jiang, Larry S. Davis
How degenerate is the parametrization of neural networks with the ReLU activation function? Dennis Maximilian Elbrächter, Julius Berner, Philipp Grohs
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks Wenrui Zhang, Peng Li
Re-examination of the Role of Latent Variables in Sequence Modeling Guokun Lai, Zihang Dai, Yiming Yang, Shinjae Yoo
Max-value Entropy Search for Multi-Objective Bayesian Optimization Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
Stein Variational Gradient Descent With Matrix-Valued Kernels Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu
Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
Detecting Overfitting via Adversarial Examples Roman Werpachowski, András György, Csaba Szepesvari
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment Felix Leibfried, Sergio Pascual-Díaz, Jordi Grau-Moya
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies Seyed Kamyar Seyed Ghasemipour, Shixiang (Shane) Gu, Richard Zemel
Towards Understanding the Importance of Shortcut Connections in Residual Networks Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains Elliot Meyerson, Risto Miikkulainen
Solving Interpretable Kernel Dimensionality Reduction Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer Dy
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space Shuo Yang, Yanyao Shen, Sujay Sanghavi
A Model to Search for Synthesizable Molecules John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin Segler, José Miguel Hernández-Lobato
Post training 4-bit quantization of convolutional networks for rapid-deployment Ron Banner, Yury Nahshan, Daniel Soudry
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner
Differentially Private Anonymized Histograms Ananda Theertha Suresh
Dynamic Local Regret for Non-convex Online Forecasting Sergul Aydore, Tianhao Zhu, Dean P. Foster
Learning Local Search Heuristics for Boolean Satisfiability Emre Yolcu, Barnabas Poczos
Provably Efficient Q-Learning with Low Switching Cost Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
Solving graph compression via optimal transport Vikas Garg, Tommi Jaakkola
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala
Stability of Graph Scattering Transforms Fernando Gama, Alejandro Ribeiro, Joan Bruna
A Debiased MDI Feature Importance Measure for Random Forests Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, Bin Yu
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle Simon S. Du, Yuping Luo, Ruosong Wang, Hanrui Zhang
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks Guodong Zhang, James Martens, Roger B. Grosse
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices Santosh Vempala, Andre Wibisono
Learning Distributions Generated by One-Layer ReLU Networks Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
Large-scale optimal transport map estimation using projection pursuit Cheng Meng, Yuan Ke, Jingyi Zhang, Mengrui Zhang, Wenxuan Zhong, Ping Ma
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning Nicolas Carion, Nicolas Usunier, Gabriel Synnaeve, Alessandro Lazaric
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Russ R. Salakhutdinov, Ruosong Wang
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning Gregory Farquhar, Shimon Whiteson, Jakob Foerster
Chirality Nets for Human Pose Regression Raymond Yeh, Yuan-Ting Hu, Alexander Schwing
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao
Fast Decomposable Submodular Function Minimization using Constrained Total Variation Senanayak Sesh Kumar Karri, Francis Bach, Thomas Pock
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George Dahl, Chris Shallue, Roger B. Grosse
Spherical Text Embedding Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han
Möbius Transformation for Fast Inner Product Search on Graph Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li
Hyperbolic Graph Neural Networks Qi Liu, Maximilian Nickel, Douwe Kiela
Average Individual Fairness: Algorithms, Generalization and Experiments Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
Fixing the train-test resolution discrepancy Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herve Jegou
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi
Manipulating a Learning Defender and Ways to Counteract Jiarui Gan, Qingyu Guo, Long Tran-Thanh, Bo An, Michael Wooldridge
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus
Learning to Infer Implicit Surfaces without 3D Supervision Shichen Liu, Shunsuke Saito, Weikai Chen, Hao Li
Fast and Accurate Least-Mean-Squares Solvers Alaa Maalouf, Ibrahim Jubran, Dan Feldman
Certifiable Robustness to Graph Perturbations Aleksandar Bojchevski, Stephan Günnemann
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay Frederic Koehler
Paradoxes in Fair Machine Learning Paul Goelz, Anson Kahng, Ariel D. Procaccia
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost Zhuoran Yang, Yongxin Chen, Mingyi Hong, Zhaoran Wang
The spiked matrix model with generative priors Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
Gradient Dynamics of Shallow Univariate ReLU Networks Francis Williams, Matthew Trager, Daniele Panozzo, Claudio Silva, Denis Zorin, Joan Bruna
Robust and Communication-Efficient Collaborative Learning Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Multiclass Learning from Contradictions Sauptik Dhar, Vladimir Cherkassky, Mohak Shah
Learning from Trajectories via Subgoal Discovery Sujoy Paul, Jeroen Vanbaar, Amit Roy-Chowdhury
Distributed Low-rank Matrix Factorization With Exact Consensus Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B. Wakin
Online Normalization for Training Neural Networks Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Koster, Ryan Reece, Sofia Samaniego de la Fuente, Vishal Subbiah, Michael James
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic Arash Ardakani, Zhengyun Ji, Amir Ardakani, Warren Gross
An adaptive Mirror-Prox method for variational inequalities with singular operators Kimon Antonakopoulos, Veronica Belmega, Panayotis Mertikopoulos
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules Shengchao Liu, Mehmet F. Demirel, Yingyu Liang
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory Bin Hu, Usman Syed
Facility Location Problem in Differential Privacy Model Revisited Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang
Energy-Inspired Models: Learning with Sampler-Induced Distributions John Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator Karl Krauth, Stephen Tu, Benjamin Recht
A Universally Optimal Multistage Accelerated Stochastic Gradient Method Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli
Large Memory Layers with Product Keys Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, Herve Jegou
Learning Deterministic Weighted Automata with Queries and Counterexamples Gail Weiss, Yoav Goldberg, Eran Yahav
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals Surbhi Goel, Sushrut Karmalkar, Adam Klivans
Visualizing and Measuring the Geometry of BERT Emily Reif, Ann Yuan, Martin Wattenberg, Fernanda B. Viegas, Andy Coenen, Adam Pearce, Been Kim
Self-Critical Reasoning for Robust Visual Question Answering Jialin Wu, Raymond Mooney
Learning to Screen Alon Cohen, Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Shay Moran
A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers Hao Yu
A Little Is Enough: Circumventing Defenses For Distributed Learning Gilad Baruch, Moran Baruch, Yoav Goldberg
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks Gunjan Verma, Ananthram Swami
A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
Finite-Sample Analysis for SARSA with Linear Function Approximation Shaofeng Zou, Tengyu Xu, Yingbin Liang
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Lenka Zdeborová
Graph Structured Prediction Energy Networks Colin Graber, Alexander Schwing
Private Learning Implies Online Learning: An Efficient Reduction Alon Gonen, Elad Hazan, Shay Moran
Graph Agreement Models for Semi-Supervised Learning Otilia Stretcu, Krishnamurthy Viswanathan, Dana Movshovitz-Attias, Emmanouil Platanios, Sujith Ravi, Andrew Tomkins
Latent distance estimation for random geometric graphs Ernesto Araya Valdivia, De Castro Yohann
Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network Jennifer Cardona, Michael Howland, John Dabiri
The Functional Neural Process Christos Louizos, Xiahan Shi, Klamer Schutte, Max Welling
Recurrent Registration Neural Networks for Deformable Image Registration Robin Sandkühler, Simon Andermatt, Grzegorz Bauman, Sylvia Nyilas, Christoph Jud, Philippe C. Cattin
Unsupervised State Representation Learning in Atari Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm
Unlocking Fairness: a Trade-off Revisited Michael Wick, swetasudha panda, Jean-Baptiste Tristan
Fisher Efficient Inference of Intractable Models Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen
Thompson Sampling and Approximate Inference My Phan, Yasin Abbasi Yadkori, Justin Domke
PRNet: Self-Supervised Learning for Partial-to-Partial Registration Yue Wang, Justin M. Solomon
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians Axel Brando, Jose A. Rodriguez, Jordi Vitria, Alberto Rubio Muñoz
Learning Macroscopic Brain Connectomes via Group-Sparse Factorization Farzane Aminmansour, Andrew Patterson, Lei Le, Yisu Peng, Daniel Mitchell, Franco Pestilli, Cesar F. Caiafa, Russell Greiner, Martha White
Approximating the Permanent by Sampling from Adaptive Partitions Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
Retrosynthesis Prediction with Conditional Graph Logic Network Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, Le Song
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon Graham
Online Learning via the Differential Privacy Lens Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu
Parameter elimination in particle Gibbs sampling Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan K. Su
Adaptively Aligned Image Captioning via Adaptive Attention Time Lun Huang, Wenmin Wang, Yaxian Xia, Jie Chen
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning Jeremiah Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull
Learning Bayesian Networks with Low Rank Conditional Probability Tables Adarsh Barik, Jean Honorio
Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth Spelke, Josh Tenenbaum, Tomer Ullman
Neural Multisensory Scene Inference Jae Hyun Lim, Pedro O. O. Pinheiro, Negar Rostamzadeh, Chris Pal, Sungjin Ahn
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems Young Hun Jung, Ambuj Tewari
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu, Yuanzhi Li
Better Transfer Learning with Inferred Successor Maps Tamas Madarasz, Tim Behrens
Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations Yifeng Fan, Tingran Gao, Zhizhen Jane Zhao
Defending Against Neural Fake News Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi
Sample Adaptive MCMC Michael Zhu
A Stochastic Composite Gradient Method with Incremental Variance Reduction Junyu Zhang, Lin Xiao
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses Ananya Uppal, Shashank Singh, Barnabas Poczos
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing Karim Ahmed, Lorenzo Torresani
Limitations of Lazy Training of Two-layers Neural Network Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
Reconciling meta-learning and continual learning with online mixtures of tasks Ghassen Jerfel, Erin Grant, Tom Griffiths, Katherine A. Heller
Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib, Stefanie Jegelka
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen, Mario Geiger, Maurice Weiler
Trivializations for Gradient-Based Optimization on Manifolds Mario Lezcano Casado
Write, Execute, Assess: Program Synthesis with a REPL Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama
A Meta-Analysis of Overfitting in Machine Learning Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs Boaz Barak, Chi-Ning Chou, Zhixian Lei, Tselil Schramm, Yueqi Sheng
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models Farnam Mansouri, Yuxin Chen, Ara Vartanian, Jerry Zhu, Adish Singla
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi
Sampling Networks and Aggregate Simulation for Online POMDP Planning Hao(Jackson) Cui, Roni Khardon
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks Gabriele Farina, Chun Kai Ling, Fei Fang, Tuomas Sandholm
GNNExplainer: Generating Explanations for Graph Neural Networks Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec
Linear Stochastic Bandits Under Safety Constraints Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
A coupled autoencoder approach for multi-modal analysis of cell types Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn, Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar Sümbül
Towards Automatic Concept-based Explanations Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim
Deep Generative Video Compression Salvator Lombardo, JUN HAN, Christopher Schroers, Stephan Mandt
Budgeted Reinforcement Learning in Continuous State Space Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin
Discovery of Useful Questions as Auxiliary Tasks Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Janarthanan Rajendran, Richard L. Lewis, Junhyuk Oh, Hado P. van Hasselt, David Silver, Satinder Singh
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna
Correlation clustering with local objectives Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou
Multiclass Performance Metric Elicitation Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O. Koyejo
Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing Zhiqi Bu, Jason Klusowski, Cynthia Rush, Weijie Su
Explicit Explore-Exploit Algorithms in Continuous State Spaces Mikael Henaff
ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls Jinjin Tian, Aaditya Ramdas
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices Vincent Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse James Lucas, George Tucker, Roger B. Grosse, Mohammad Norouzi
Language as an Abstraction for Hierarchical Deep Reinforcement Learning YiDing Jiang, Shixiang (Shane) Gu, Kevin P. Murphy, Chelsea Finn
Efficient online learning with kernels for adversarial large scale problems Rémi Jézéquel, Pierre Gaillard, Alessandro Rudi
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning Zhihui Zhu, Tianyu Ding, Daniel Robinson, Manolis Tsakiris, René Vidal
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz
Certified Adversarial Robustness with Additive Noise Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels Michela Meister, Tamas Sarlos, David Woodruff
Non-Cooperative Inverse Reinforcement Learning Xiangyuan Zhang, Kaiqing Zhang, Erik Miehling, Tamer Basar
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization Rixon Crane, Fred Roosta
Sobolev Independence Criterion Youssef Mroueh, Tom Sercu, Mattia Rigotti, Inkit Padhi, Cicero Nogueira dos Santos
Maximum Entropy Monte-Carlo Planning Chenjun Xiao, Ruitong Huang, Jincheng Mei, Dale Schuurmans, Martin Müller
Learning from brains how to regularize machines Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian Sinz, Zachary Pitkow, Andreas Tolias
Using Statistics to Automate Stochastic Optimization Hunter Lang, Lin Xiao, Pengchuan Zhang
Zero-shot Knowledge Transfer via Adversarial Belief Matching Paul Micaelli, Amos J. Storkey
Differentiable Convex Optimization Layers Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, J. Zico Kolter
Random Tessellation Forests Shufei Ge, Shijia Wang, Yee Whye Teh, Liangliang Wang, Lloyd Elliott
Learning Nearest Neighbor Graphs from Noisy Distance Samples Blake Mason, Ardhendu Tripathy, Robert Nowak
Lookahead Optimizer: k steps forward, 1 step back Michael Zhang, James Lucas, Jimmy Ba, Geoffrey E. Hinton
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer Wenzheng Chen, Huan Ling, Jun Gao, Edward Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
Covariate-Powered Empirical Bayes Estimation Nikolaos Ignatiadis, Stefan Wager
Understanding the Role of Momentum in Stochastic Gradient Methods Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao
A neurally plausible model for online recognition and postdiction in a dynamical environment Li Kevin Wenliang, Maneesh Sahani
Guided Meta-Policy Search Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling Tengyang Xie, Yifei Ma, Yu-Xiang Wang
Contextual Bandits with Cross-Learning Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider
Evaluating Protein Transfer Learning with TAPE Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, Yun Song
A Bayesian Theory of Conformity in Collective Decision Making Koosha Khalvati, Saghar Mirbagheri, Seongmin A. Park, Jean-Claude Dreher, Rajesh PN Rao
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei, Tengyu Ma
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
Memory Efficient Adaptive Optimization Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions Negin Golrezaei, Adel Javanmard, Vahab Mirrokni
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control Miguel Vaquero, Jorge Cortes
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning Xuanqing Liu, Si Si, Jerry Zhu, Yang Li, Cho-Jui Hsieh
Compositional generalization through meta sequence-to-sequence learning Brenden M. Lake
Bayesian Joint Estimation of Multiple Graphical Models Lingrui Gan, Xinming Yang, Naveen Narisetty, Feng Liang
Practical Two-Step Lookahead Bayesian Optimization Jian Wu, Peter Frazier
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models Yunfei Teng, Wenbo Gao, François Chalus, Anna E. Choromanska, Donald Goldfarb, Adrian Weller
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, Pengchuan Zhang
Neural Jump Stochastic Differential Equations Junteng Jia, Austin R. Benson
Learning metrics for persistence-based summaries and applications for graph classification Qi Zhao, Yusu Wang
On the Value of Target Data in Transfer Learning Steve Hanneke, Samory Kpotufe
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization Adithya M Devraj, Jianshu Chen
On Robustness of Principal Component Regression Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song
Meta Learning with Relational Information for Short Sequences Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha
Residual Flows for Invertible Generative Modeling Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
Multi-Agent Common Knowledge Reinforcement Learning Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer, Shimon Whiteson
Learning to Learn By Self-Critique Antreas Antoniou, Amos J. Storkey
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes Greg Yang
Neural Networks with Cheap Differential Operators Tian Qi Chen, David K. Duvenaud
Transductive Zero-Shot Learning with Visual Structure Constraint Ziyu Wan, Dongdong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao
Dying Experts: Efficient Algorithms with Optimal Regret Bounds Hamid Shayestehmanesh, Sajjad Azami, Nishant A. Mehta
Model Similarity Mitigates Test Set Overuse Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht
A unified theory for the origin of grid cells through the lens of pattern formation Ben Sorscher, Gabriel Mel, Surya Ganguli, Samuel Ocko
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons Wenbo Ren, Jia (Kevin) Liu, Ness Shroff
Hierarchical Decision Making by Generating and Following Natural Language Instructions Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data Qian Lou, Lei Jiang
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni
A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang, Yang Zhang, Mo Yu, Tommi Jaakkola
Efficiently avoiding saddle points with zero order methods: No gradients required Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Metamers of neural networks reveal divergence from human perceptual systems Jenelle Feather, Alex Durango, Ray Gonzalez, Josh McDermott
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization Yujiao Shi, Liu Liu, Xin Yu, Hongdong Li
Decentralized sketching of low rank matrices Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg
Average Case Column Subset Selection for Entrywise \ell_1-Norm Loss Zhao Song, David Woodruff, Peilin Zhong
Efficient Forward Architecture Search Hanzhang Hu, John Langford, Rich Caruana, Saurajit Mukherjee, Eric J. Horvitz, Debadeepta Dey
Unsupervised Meta-Learning for Few-Shot Image Classification Siavash Khodadadeh, Ladislau Boloni, Mubarak Shah
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders Zhibing Zhao, Lirong Xia
Certainty Equivalence is Efficient for Linear Quadratic Control Horia Mania, Stephen Tu, Benjamin Recht
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski
Logarithmic Regret for Online Control Naman Agarwal, Elad Hazan, Karan Singh
Elliptical Perturbations for Differential Privacy Matthew Reimherr, Jordan Awan
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, Yang Liu
KNG: The K-Norm Gradient Mechanism Matthew Reimherr, Jordan Awan
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab, Walter Karlen
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang
STREETS: A Novel Camera Network Dataset for Traffic Flow Corey Snyder, Minh Do
Sequential Neural Processes Gautam Singh, Jaesik Yoon, Youngsung Son, Sungjin Ahn
Policy Continuation with Hindsight Inverse Dynamics Hao Sun, Zhizhong Li, Xiaotong Liu, Bolei Zhou, Dahua Lin
Learning to Self-Train for Semi-Supervised Few-Shot Classification Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W. Koh, Stefano Ermon
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization Krzysztof M. Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
On the Expressive Power of Deep Polynomial Neural Networks Joe Kileel, Matthew Trager, Joan Bruna
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Can SGD Learn Recurrent Neural Networks with Provable Generalization? Zeyuan Allen-Zhu, Yuanzhi Li
Limits of Private Learning with Access to Public Data Raef Bassily, Shay Moran, Noga Alon
Discrete Object Generation with Reversible Inductive Construction Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing Larkin Flodin, Venkata Gandikota, Arya Mazumdar
Bandits with Feedback Graphs and Switching Costs Raman Arora, Teodor Vanislavov Marinov, Mehryar Mohri
Functional Adversarial Attacks Cassidy Laidlaw, Soheil Feizi
Statistical-Computational Tradeoff in Single Index Models Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
On Fenchel Mini-Max Learning Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy Bobashev, Lawrence Carin
MarginGAN: Adversarial Training in Semi-Supervised Learning Jinhao Dong, Tong Lin
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
A unified variance-reduced accelerated gradient method for convex optimization Guanghui Lan, Zhize Li, Yi Zhou
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi
Same-Cluster Querying for Overlapping Clusters Wasim Huleihel, Arya Mazumdar, Muriel Medard, Soumyabrata Pal
Efficient Convex Relaxations for Streaming PCA Raman Arora, Teodor Vanislavov Marinov
Learning Robust Global Representations by Penalizing Local Predictive Power Haohan Wang, Songwei Ge, Zachary Lipton, Eric P. Xing
Unsupervised Curricula for Visual Meta-Reinforcement Learning Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn
Sample Complexity of Learning Mixture of Sparse Linear Regressions Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Large Scale Adversarial Representation Learning Jeff Donahue, Karen Simonyan
G2SAT: Learning to Generate SAT Formulas Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, Jure Leskovec
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
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Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
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Deep Gamblers: Learning to Abstain with Portfolio Theory Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R. Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples Tengyu Xu, Shaofeng Zou, Yingbin Liang
Transfer Learning via Minimizing the Performance Gap Between Domains Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton
Splitting Steepest Descent for Growing Neural Architectures Lemeng Wu, Dilin Wang, Qiang Liu
Sequential Experimental Design for Transductive Linear Bandits Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian Ratliff
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence Aditya Sharad Golatkar, Alessandro Achille, Stefano Soatto
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart
Variational Graph Recurrent Neural Networks Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
Semi-Implicit Graph Variational Auto-Encoders Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
Unsupervised Learning of Object Keypoints for Perception and Control Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation Xueying Bai, Jian Guan, Hongning Wang
Optimizing Generalized Rate Metrics with Three Players Harikrishna Narasimhan, Andrew Cotter, Maya Gupta
Consistency-based Semi-supervised Learning for Object detection Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak
Rates of Convergence for Large-scale Nearest Neighbor Classification Xingye Qiao, Jiexin Duan, Guang Cheng
An Embedding Framework for Consistent Polyhedral Surrogates Jessica Finocchiaro, Rafael Frongillo, Bo Waggoner
Cross-Modal Learning with Adversarial Samples CHAO LI, Shangqian Gao, Cheng Deng, De Xie, Wei Liu
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes Jun Yang, Shengyang Sun, Daniel M. Roy
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa, Hector Corrada Bravo, Soheil Feizi
Program Synthesis and Semantic Parsing with Learned Code Idioms Eui Chul Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao, Quanquan Gu
High-Dimensional Optimization in Adaptive Random Subspaces Jonathan Lacotte, Mert Pilanci, Marco Pavone
Random Projections with Asymmetric Quantization Xiaoyun Li, Ping Li
Superposition of many models into one Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen
Private Testing of Distributions via Sample Permutations Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds Rui (Ray) Zhang, Xingwu Liu, Yuyi Wang, Liwei Wang
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets Devansh Arpit, Víctor Campos, Yoshua Bengio
On Making Stochastic Classifiers Deterministic Andrew Cotter, Maya Gupta, Harikrishna Narasimhan
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection Xiaoyi Gu, Leman Akoglu, Alessandro Rinaldo
Improving Black-box Adversarial Attacks with a Transfer-based Prior Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup
Statistical Model Aggregation via Parameter Matching Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
On the (In)fidelity and Sensitivity of Explanations Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I. Inouye, Pradeep K. Ravikumar
Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
The Broad Optimality of Profile Maximum Likelihood Yi Hao, Alon Orlitsky
MintNet: Building Invertible Neural Networks with Masked Convolutions Yang Song, Chenlin Meng, Stefano Ermon
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
On Distributed Averaging for Stochastic k-PCA Aditya Bhaskara, Pruthuvi Maheshakya Wijewardena
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation Ke Wang, Hang Hua, Xiaojun Wan
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking Sumeet Katariya, Ardhendu Tripathy, Robert Nowak
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J. Horvitz, Stefano Ermon
Online Forecasting of Total-Variation-bounded Sequences Dheeraj Baby, Yu-Xiang Wang
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum Shreyas Saxena, Oncel Tuzel, Dennis DeCoste
Unified Sample-Optimal Property Estimation in Near-Linear Time Yi Hao, Alon Orlitsky
Region Mutual Information Loss for Semantic Segmentation Shuai Zhao, Yang Wang, Zheng Yang, Deng Cai
Learning Stable Deep Dynamics Models J. Zico Kolter, Gaurav Manek
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Greedy Sampling for Approximate Clustering in the Presence of Outliers Aditya Bhaskara, Sharvaree Vadgama, Hong Xu
Adversarial Fisher Vectors for Unsupervised Representation Learning Shuangfei Zhai, Walter Talbott, Carlos Guestrin, Joshua Susskind
On Tractable Computation of Expected Predictions Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
Levenshtein Transformer Jiatao Gu, Changhan Wang, Junbo Zhao
Unlabeled Data Improves Adversarial Robustness Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy S. Liang
Machine Teaching of Active Sequential Learners Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
Gaussian-Based Pooling for Convolutional Neural Networks Takumi Kobayashi
Meta Architecture Search Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test Lizhong Ding, Mengyang Yu, Li Liu, Fan Zhu, Yong Liu, Yu Li, Ling Shao
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
Private Stochastic Convex Optimization with Optimal Rates Raef Bassily, Vitaly Feldman, Kunal Talwar, Abhradeep Guha Thakurta
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, Greg Yang
Demystifying Black-box Models with Symbolic Metamodels Ahmed M. Alaa, Mihaela van der Schaar
Neural Temporal-Difference Learning Converges to Global Optima Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces Baoxiang Wang, Nidhi Hegde
Attentive State-Space Modeling of Disease Progression Ahmed M. Alaa, Mihaela van der Schaar
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback Ilai Bistritz, Zhengyuan Zhou, Xi Chen, Nicholas Bambos, Jose Blanchet
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport Arun Jambulapati, Aaron Sidford, Kevin Tian
Faster Boosting with Smaller Memory Julaiti Alafate, Yoav S. Freund
Variance Reduction for Matrix Games Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian
Learning Neural Networks with Adaptive Regularization Han Zhao, Yao-Hung Hubert Tsai, Russ R. Salakhutdinov, Geoffrey J. Gordon
Distributed estimation of the inverse Hessian by determinantal averaging Michal Derezinski, Michael W. Mahoney
Smoothing Structured Decomposable Circuits Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George Pappas
Provable Non-linear Inductive Matrix Completion Kai Zhong, Zhao Song, Prateek Jain, Inderjit S. Dhillon
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback Shuai Zheng, Ziyue Huang, James Kwok
Sparse Variational Inference: Bayesian Coresets from Scratch Trevor Campbell, Boyan Beronov
Personalizing Many Decisions with High-Dimensional Covariates Nima Hamidi, Mohsen Bayati, Kapil Gupta
A Necessary and Sufficient Stability Notion for Adaptive Generalization Moshe Shenfeld, Katrina Ligett
Necessary and Sufficient Geometries for Gradient Methods Daniel Levy, John C. Duchi
Landmark Ordinal Embedding Nikhil Ghosh, Yuxin Chen, Yisong Yue
Identification of Conditional Causal Effects under Markov Equivalence Amin Jaber, Jiji Zhang, Elias Bareinboim
The Thermodynamic Variational Objective Vaden Masrani, Tuan Anh Le, Frank Wood
Global Guarantees for Blind Demodulation with Generative Priors Paul Hand, Babhru Joshi
Exact sampling of determinantal point processes with sublinear time preprocessing Michal Derezinski, Daniele Calandriello, Michal Valko
Geometry-Aware Neural Rendering Joshua Tobin, Wojciech Zaremba, Pieter Abbeel
Variational Temporal Abstraction Taesup Kim, Sungjin Ahn, Yoshua Bengio
Subquadratic High-Dimensional Hierarchical Clustering Amir Abboud, Vincent Cohen-Addad, Hussein Houdrouge
Learning Auctions with Robust Incentive Guarantees Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie H. Morgenstern
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games Kaiqing Zhang, Zhuoran Yang, Tamer Basar
Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan, J. Zico Kolter
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid
DTWNet: a Dynamic Time Warping Network Xingyu Cai, Tingyang Xu, Jinfeng Yi, Junzhou Huang, Sanguthevar Rajasekaran
Structured Graph Learning Via Laplacian Spectral Constraints Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar
Thresholding Bandit with Optimal Aggregate Regret Chao Tao, Saúl Blanco, Jian Peng, Yuan Zhou
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks Yuanzhi Li, Colin Wei, Tengyu Ma
Rethinking Kernel Methods for Node Representation Learning on Graphs Yu Tian, Long Zhao, Xi Peng, Dimitris Metaxas
Causal Confusion in Imitation Learning Pim de Haan, Dinesh Jayaraman, Sergey Levine
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection Pan Li, I Chien, Olgica Milenkovic
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data Amanda Gentzel, Dan Garant, David Jensen
Dimension-Free Bounds for Low-Precision Training Zheng Li, Christopher M. De Sa
Concentration of risk measures: A Wasserstein distance approach Sanjay P. Bhat, Prashanth L.A.
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine
Bayesian Optimization with Unknown Search Space Huong Ha, Santu Rana, Sunil Gupta, Thanh Nguyen, Hung Tran-The, Svetha Venkatesh
On the Downstream Performance of Compressed Word Embeddings Avner May, Jian Zhang, Tri Dao, Christopher Ré
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss Jose Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou
Neural Relational Inference with Fast Modular Meta-learning Ferran Alet, Erica Weng, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Gradient based sample selection for online continual learning Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio
Attribution-Based Confidence Metric For Deep Neural Networks Susmit Jha, Sunny Raj, Steven Fernandes, Sumit K. Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami
Theoretical evidence for adversarial robustness through randomization Rafael Pinot, Laurent Meunier, Alexandre Araujo, Hisashi Kashima, Florian Yger, Cedric Gouy-Pailler, Jamal Atif
Online Continual Learning with Maximal Interfered Retrieval Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia
Neural Attribution for Semantic Bug-Localization in Student Programs Rahul Gupta, Aditya Kanade, Shirish Shevade
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, Andre Barreto, Gergely Neu
SPoC: Search-based Pseudocode to Code Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, Percy S. Liang
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song, Stefano Ermon
Adversarial Music: Real world Audio Adversary against Wake-word Detection System Juncheng Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees Muhammad Osama, Dave Zachariah, Peter Stoica
Debiased Bayesian inference for average treatment effects Kolyan Ray, Botond Szabo
Margin-Based Generalization Lower Bounds for Boosted Classifiers Allan Grønlund, Lior Kamma, Kasper Green Larsen, Alexander Mathiasen, Jelani Nelson
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio Julian Zimmert, Tor Lattimore
Graph Transformer Networks Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou
The Impact of Regularization on High-dimensional Logistic Regression Fariborz Salehi, Ehsan Abbasi, Babak Hassibi
Adaptive Density Estimation for Generative Models Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
Fast and Provable ADMM for Learning with Generative Priors Fabian Latorre, Armin eftekhari, Volkan Cevher
Weighted Linear Bandits for Non-Stationary Environments Yoan Russac, Claire Vernade, Olivier Cappé
Improved Regret Bounds for Bandit Combinatorial Optimization Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi
Pareto Multi-Task Learning Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, Sam Kwong
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits Etienne Boursier, Vianney Perchet
Novel positional encodings to enable tree-based transformers Vighnesh Shiv, Chris Quirk
A Domain Agnostic Measure for Monitoring and Evaluating GANs Paulina Grnarova, Kfir Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause
Submodular Function Minimization with Noisy Evaluation Oracle Shinji Ito
Counting the Optimal Solutions in Graphical Models Radu Marinescu, Rina Dechter
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach Shuyue Hu, Chin-wing Leung, Ho-fung Leung
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling Ming Hou, Jiajia Tang, Jianhai Zhang, Wanzeng Kong, Qibin Zhao
Bootstrapping Upper Confidence Bound Botao Hao, Yasin Abbasi Yadkori, Zheng Wen, Guang Cheng
Integer Discrete Flows and Lossless Compression Emiel Hoogeboom, Jorn Peters, Rianne van den Berg, Max Welling
Structured Prediction with Projection Oracles Mathieu Blondel
A Primal Dual Formulation For Deep Learning With Constraints Yatin Nandwani, Abhishek Pathak, Mausam, Parag Singla
Screening Sinkhorn Algorithm for Regularized Optimal Transport Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy
PAC-Bayes Un-Expected Bernstein Inequality Zakaria Mhammedi, Peter Grünwald, Benjamin Guedj
Are Labels Required for Improving Adversarial Robustness? Jean-Baptiste Alayrac, Jonathan Uesato, Po-Sen Huang, Alhussein Fawzi, Robert Stanforth, Pushmeet Kohli
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor
Multi-objective Bayesian optimisation with preferences over objectives Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging Pooria Joulani, András György, Csaba Szepesvari
Calibration tests in multi-class classification: A unifying framework David Widmann, Fredrik Lindsten, Dave Zachariah
Classification Accuracy Score for Conditional Generative Models Suman Ravuri, Oriol Vinyals
Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu, Jingwei Zhang, Dacheng Tao
Multiagent Evaluation under Incomplete Information Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Perolat, Michal Valko, Georgios Piliouras, Remi Munos
Tree-Sliced Variants of Wasserstein Distances Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
Comparing distributions: \ell_1 geometry improves kernel two-sample testing meyer scetbon, Gael Varoquaux
Robustness Verification of Tree-based Models Hongge Chen, Huan Zhang, Si Si, Yang Li, Duane Boning, Cho-Jui Hsieh
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende
Fast and Accurate Stochastic Gradient Estimation Beidi Chen, Yingchen Xu, Anshumali Shrivastava
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning Igor Colin, Ludovic DOS SANTOS, Kevin Scaman
Root Mean Square Layer Normalization Biao Zhang, Rico Sennrich
Universality in Learning from Linear Measurements Ehsan Abbasi, Fariborz Salehi, Babak Hassibi
Planning in entropy-regularized Markov decision processes and games Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko
Exponentially convergent stochastic k-PCA without variance reduction Cheng Tang
R2D2: Reliable and Repeatable Detector and Descriptor Jerome Revaud, Cesar De Souza, Martin Humenberger, Philippe Weinzaepfel
Selective Sampling-based Scalable Sparse Subspace Clustering Shin Matsushima, Maria Brbic
A General Framework for Symmetric Property Estimation Moses Charikar, Kirankumar Shiragur, Aaron Sidford
Structured Variational Inference in Continuous Cox Process Models Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas, Sally Cripps
Generalization of Reinforcement Learners with Working and Episodic Memory Meire Fortunato, Melissa Tan, Ryan Faulkner, Steven Hansen, Adrià Puigdomènech Badia, Gavin Buttimore, Charles Deck, Joel Z. Leibo, Charles Blundell
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor Meera Pai, Animesh Kumar
Hindsight Credit Assignment Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P. van Hasselt, Gregory Wayne, Satinder Singh, Doina Precup, Remi Munos
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets Daniel Kumor, Bryant Chen, Elias Bareinboim
Kernelized Bayesian Softmax for Text Generation Ning Miao, Hao Zhou, Chengqi Zhao, Wenxian Shi, Lei Li
When to Trust Your Model: Model-Based Policy Optimization Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine
Correlation Clustering with Adaptive Similarity Queries Marco Bressan, Nicolò Cesa-Bianchi, Andrea Paudice, Fabio Vitale
Control What You Can: Intrinsically Motivated Task-Planning Agent Sebastian Blaes, Marin Vlastelica Pogančić, Jiajie Zhu, Georg Martius
Selecting causal brain features with a single conditional independence test per feature Atalanti Mastakouri, Bernhard Schölkopf, Dominik Janzing
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
A Generic Acceleration Framework for Stochastic Composite Optimization Andrei Kulunchakov, Julien Mairal
Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Muecke, Gergely Neu, Lorenzo Rosasco
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Steven Z. Wu
Continuous-time Models for Stochastic Optimization Algorithms Antonio Orvieto, Aurelien Lucchi
Curriculum-guided Hindsight Experience Replay Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang
Implicit Semantic Data Augmentation for Deep Networks Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, Cheng Wu
MetaInit: Initializing learning by learning to initialize Yann N. Dauphin, Samuel Schoenholz
Scalable Deep Generative Relational Model with High-Order Node Dependence Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Random Path Selection for Continual Learning Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
Efficient Algorithms for Smooth Minimax Optimization Kiran K. Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Shadowing Properties of Optimization Algorithms Antonio Orvieto, Aurelien Lucchi
Causal Regularization Dominik Janzing
Learning Hawkes Processes from a handful of events Farnood Salehi, William Trouleau, Matthias Grossglauser, Patrick Thiran
Unsupervised Object Segmentation by Redrawing Mickaël Chen, Thierry Artières, Ludovic Denoyer
Regret Bounds for Learning State Representations in Reinforcement Learning Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Band-Limited Gaussian Processes: The Sinc Kernel Felipe Tobar
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning Valerio Perrone, Huibin Shen
Streaming Bayesian Inference for Crowdsourced Classification Edoardo Manino, Long Tran-Thanh, Nicholas Jennings
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellstrom, Cheng Zhang
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib Majaj, Rishi Rajalingham, Elias Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. Yamins, James J. DiCarlo
k-Means Clustering of Lines for Big Data Yair Marom, Dan Feldman
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves Stefan Meintrup, Alexander Munteanu, Dennis Rohde
Recurrent Space-time Graph Neural Networks Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu
Uncertainty on Asynchronous Time Event Prediction Bertrand Charpentier, Marin Biloš, Stephan Günnemann
Accurate, reliable and fast robustness evaluation Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge
Sparse High-Dimensional Isotonic Regression David Gamarnik, Julia Gaudio
Triad Constraints for Learning Causal Structure of Latent Variables Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
On the Inductive Bias of Neural Tangent Kernels Alberto Bietti, Julien Mairal
Cross-Domain Transferability of Adversarial Perturbations Muhammad Muzammal Naseer, Salman H. Khan, Muhammad Haris Khan, Fahad Shahbaz Khan, Fatih Porikli
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain
Kernel quadrature with DPPs Ayoub Belhadji, Rémi Bardenet, Pierre Chainais
REM: From Structural Entropy to Community Structure Deception Yiwei Liu, Jiamou Liu, Zijian Zhang, Liehuang Zhu, Angsheng Li
Sim2real transfer learning for 3D human pose estimation: motion to the rescue Carl Doersch, Andrew Zisserman
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space Bjarne Sievers, Jonathan Sauder
Piecewise Strong Convexity of Neural Networks Tristan Milne
Minimum Stein Discrepancy Estimators Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, Lester Mackey
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes James Bailey, Georgios Piliouras
Generalization Bounds for Neural Networks via Approximate Description Length Amit Daniely, Elad Granot
Provably robust boosted decision stumps and trees against adversarial attacks Maksym Andriushchenko, Matthias Hein
Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao, Tianle Cai, Haochuan Li, Cho-Jui Hsieh, Liwei Wang, Jason D. Lee
A Composable Specification Language for Reinforcement Learning Tasks Kishor Jothimurugan, Rajeev Alur, Osbert Bastani
The Option Keyboard: Combining Skills in Reinforcement Learning Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan hunt, Shibl Mourad, David Silver, Doina Precup
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon
Learning to Correlate in Multi-Player General-Sum Sequential Games Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen
Generative Well-intentioned Networks Justin Cosentino, Jun Zhu
Online-Within-Online Meta-Learning Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil
Learning step sizes for unfolded sparse coding Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort
Biases for Emergent Communication in Multi-agent Reinforcement Learning Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel
Episodic Memory in Lifelong Language Learning Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Communication-efficient Distributed SGD with Sketching Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir braverman, Ion Stoica, Raman Arora
Modeling Conceptual Understanding in Image Reference Games Rodolfo Corona Rodriguez, Stephan Alaniz, Zeynep Akata
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights Maria Jahja, David Farrow, Roni Rosenfeld, Ryan J. Tibshirani
Near Neighbor: Who is the Fairest of Them All? Sariel Har-Peled, Sepideh Mahabadi
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator Arnak Dalalyan, Philip Thompson
Learning nonlinear level sets for dimensionality reduction in function approximation Guannan Zhang, Jiaxin Zhang, Jacob Hinkle
Assessing Social and Intersectional Biases in Contextualized Word Representations Yi Chern Tan, L. Elisa Celis
Online Convex Matrix Factorization with Representative Regions Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game Ngoc-Trung Tran, Viet-Hung Tran, Bao-Ngoc Nguyen, Linxiao Yang, Ngai-Man (Man) Cheung
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products Tharun Kumar Reddy Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, Anshumali Shrivastava
A Fourier Perspective on Model Robustness in Computer Vision Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer
The continuous Bernoulli: fixing a pervasive error in variational autoencoders Gabriel Loaiza-Ganem, John P. Cunningham
Privacy Amplification by Mixing and Diffusion Mechanisms Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek
Variance Reduction in Bipartite Experiments through Correlation Clustering Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning Mahmoud ("Mido") Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Mike Rabbat
Metalearned Neural Memory Tsendsuren Munkhdalai, Alessandro Sordoni, TONG WANG, Adam Trischler
Learning Multiple Markov Chains via Adaptive Allocation Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Diffusion Improves Graph Learning Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark Churchland, John P. Cunningham
Variational Bayes under Model Misspecification Yixin Wang, David Blei
Global Convergence of Gradient Descent for Deep Linear Residual Networks Lei Wu, Qingcan Wang, Chao Ma
On Differentially Private Graph Sparsification and Applications Raman Arora, Jalaj Upadhyay
Manifold denoising by Nonlinear Robust Principal Component Analysis He Lyu, Ningyu Sha, Shuyang Qin, Ming Yan, Yuying Xie, Rongrong Wang
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes Junzhe Zhang, Elias Bareinboim
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks Cagatay Yildiz, Markus Heinonen, Harri Lahdesmaki
Optimal Sampling and Clustering in the Stochastic Block Model Se-Young Yun, Alexandre Proutiere
Recurrent Kernel Networks Dexiong Chen, Laurent Jacob, Julien Mairal
Cold Case: The Lost MNIST Digits Chhavi Yadav, Leon Bottou
Hierarchical Optimal Transport for Multimodal Distribution Alignment John Lee, Max Dabagia, Eva Dyer, Christopher Rozell
Exploration via Hindsight Goal Generation Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
Shaping Belief States with Generative Environment Models for RL Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aaron van den Oord
Globally Optimal Learning for Structured Elliptical Losses Yoav Wald, Nofar Noy, Gal Elidan, Ami Wiesel
Object landmark discovery through unsupervised adaptation Enrique Sanchez, Georgios Tzimiropoulos
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions Ashia C. Wilson, Lester Mackey, Andre Wibisono
RUDDER: Return Decomposition for Delayed Rewards Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
Graph Normalizing Flows Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel
Communication trade-offs for Local-SGD with large step size Aymeric Dieuleveut, Kumar Kshitij Patel
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie
No-Regret Learning in Unknown Games with Correlated Payoffs Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause
Alleviating Label Switching with Optimal Transport Pierre Monteiller, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, Justin M. Solomon, Mikhail Yurochkin
Paraphrase Generation with Latent Bag of Words Yao Fu, Yansong Feng, John P. Cunningham
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
Compacting, Picking and Growing for Unforgetting Continual Learning Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Agata Lapedriza, Rosalind Picard
A New Distribution on the Simplex with Auto-Encoding Applications Andrew Stirn, Tony Jebara, David Knowles
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters XIA XIAO, Zigeng Wang, Sanguthevar Rajasekaran
A neurally plausible model learns successor representations in partially observable environments Eszter Vértes, Maneesh Sahani
Learning about an exponential amount of conditional distributions Mohamed Belghazi, Maxime Oquab, David Lopez-Paz
Towards modular and programmable architecture search Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon, Darshan Patil, Nghia Le, Daniel Ferreira
Towards Hardware-Aware Tractable Learning of Probabilistic Models Laura I. Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck
On Robustness to Adversarial Examples and Polynomial Optimization Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, Rohan Kadekodi
A Solvable High-Dimensional Model of GAN Chuang Wang, Hong Hu, Yue Lu
Using Embeddings to Correct for Unobserved Confounding in Networks Victor Veitch, Yixin Wang, David Blei
MonoForest framework for tree ensemble analysis Igor Kuralenok, Vasilii Ershov, Igor Labutin
Bayesian Optimization under Heavy-tailed Payoffs Sayak Ray Chowdhury, Aditya Gopalan
Combining Generative and Discriminative Models for Hybrid Inference Victor Garcia Satorras, Max Welling, Zeynep Akata
A Graph Theoretic Additive Approximation of Optimal Transport Nathaniel Lahn, Deepika Mulchandani, Sharath Raghvendra
Adversarial Robustness through Local Linearization Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Krishnamurthy Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli
Sampled Softmax with Random Fourier Features Ankit Singh Rawat, Jiecao Chen, Felix Xinnan X. Yu, Ananda Theertha Suresh, Sanjiv Kumar
Semi-flat minima and saddle points by embedding neural networks to overparameterization Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka
Learning Fairness in Multi-Agent Systems Jiechuan Jiang, Zongqing Lu
Primal-Dual Block Generalized Frank-Wolfe Qi Lei, JIACHENG ZHUO, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis
GOT: An Optimal Transport framework for Graph comparison Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak
Complexity of Highly Parallel Non-Smooth Convex Optimization Sebastien Bubeck, Qijia Jiang, Yin-Tat Lee, Yuanzhi Li, Aaron Sidford
Inverting Deep Generative models, One layer at a time Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
The Implicit Metropolis-Hastings Algorithm Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints Mehmet Fatih Sahin, Armin eftekhari, Ahmet Alacaoglu, Fabian Latorre, Volkan Cevher
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua Dillon, Jie Ren, Zachary Nado
Accurate Layerwise Interpretable Competence Estimation Vickram Rajendran, William LeVine
A New Perspective on Pool-Based Active Classification and False-Discovery Control Lalit Jain, Kevin G. Jamieson
Defending Neural Backdoors via Generative Distribution Modeling Ximing Qiao, Yukun Yang, Hai Li
Are Sixteen Heads Really Better than One? Paul Michel, Omer Levy, Graham Neubig
Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami
Variational Bayesian Optimal Experimental Design Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, Noah Goodman
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets Joshua Hanson, Maxim Raginsky
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes Matt Jordan, Justin Lewis, Alexandros G. Dimakis
Reinforcement Learning with Convex Constraints Sobhan Miryoosefi, Kianté Brantley, Hal Daume III, Miro Dudik, Robert E. Schapire
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning Dirk van der Hoeven
Stochastic Bandits with Context Distributions Johannes Kirschner, Andreas Krause
Inducing brain-relevant bias in natural language processing models Dan Schwartz, Mariya Toneva, Leila Wehbe
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning Harm Van Seijen, Mehdi Fatemi, Arash Tavakoli
Recovering Bandits Ciara Pike-Burke, Steffen Grunewalder
Computing Linear Restrictions of Neural Networks Matthew Sotoudeh, Aditya V. Thakur
Learning Positive Functions with Pseudo Mirror Descent Yingxiang Yang, Haoxiang Wang, Negar Kiyavash, Niao He
Correlation Priors for Reinforcement Learning Bastian Alt, Adrian Šošić, Heinz Koeppl
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression Deeksha Adil, Richard Peng, Sushant Sachdeva
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit Yanis Bahroun, Dmitri Chklovskii, Anirvan Sengupta
Differentially Private Covariance Estimation Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii
Outlier Detection and Robust PCA Using a Convex Measure of Innovation Mostafa Rahmani, Ping Li
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems Robert Ness, Kaushal Paneri, Olga Vitek
Are Disentangled Representations Helpful for Abstract Visual Reasoning? Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Stochastic Frank-Wolfe for Composite Convex Minimization Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher
Constraint-based Causal Structure Learning with Consistent Separating Sets Honghao Li, Vincent Cabeli, Nadir Sella, Herve Isambert
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis David Clark, Jesse Livezey, Kristofer Bouchard
Sample Efficient Active Learning of Causal Trees Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection Junran Peng, Ming Sun, ZHAO-XIANG ZHANG, Tieniu Tan, Junjie Yan
Robust Attribution Regularization Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization Miika Aittala, Prafull Sharma, Lukas Murmann, Adam Yedidia, Gregory Wornell, Bill Freeman, Fredo Durand
When to use parametric models in reinforcement learning? Hado P. van Hasselt, Matteo Hessel, John Aslanides
General E(2)-Equivariant Steerable CNNs Maurice Weiler, Gabriele Cesa
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
Structure Learning with Side Information: Sample Complexity Saurabh Sihag, Ali Tajer
Untangling in Invariant Speech Recognition Cory Stephenson, Jenelle Feather, Suchismita Padhy, Oguz Elibol, Hanlin Tang, Josh McDermott, SueYeon Chung
Flexible information routing in neural populations through stochastic comodulation Caroline Haimerl, Cristina Savin, Eero Simoncelli
Generalization Bounds in the Predict-then-Optimize Framework Othman El Balghiti, Adam Elmachtoub, Paul Grigas, Ambuj Tewari
Categorized Bandits Matthieu Jedor, Vianney Perchet, Jonathan Louedec
Worst-Case Regret Bounds for Exploration via Randomized Value Functions Daniel Russo
Efficient characterization of electrically evoked responses for neural interfaces Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky
Differentially Private Distributed Data Summarization under Covariate Shift Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, Roman Vaculin
Hamiltonian descent for composite objectives Brendan O'Donoghue, Chris J. Maddison
Implicit Regularization of Accelerated Methods in Hilbert Spaces Nicolò Pagliana, Lorenzo Rosasco
Non-Asymptotic Pure Exploration by Solving Games Rémy Degenne, Wouter M. Koolen, Pierre Ménard
Implicit Posterior Variational Inference for Deep Gaussian Processes Haibin YU, Yizhou Chen, Bryan Kian Hsiang Low, Patrick Jaillet, Zhongxiang Dai
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces Benyamin Allahgholizadeh Haghi, Spencer Kellis, Sahil Shah, Maitreyi Ashok, Luke Bashford, Daniel Kramer, Brian Lee, Charles Liu, Richard Andersen, Azita Emami
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback Arun Verma, Manjesh Hanawal, Arun Rajkumar, Raman Sankaran
Cormorant: Covariant Molecular Neural Networks Brandon Anderson, Truong Son Hy, Risi Kondor
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness Andrey Malinin, Mark Gales
Reflection Separation using a Pair of Unpolarized and Polarized Images Youwei Lyu, Zhaopeng Cui, Si Li, Marc Pollefeys, Boxin Shi
Policy Poisoning in Batch Reinforcement Learning and Control Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees Alix LHERITIER, Frederic Cazals
Pure Exploration with Multiple Correct Answers Rémy Degenne, Wouter M. Koolen
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora
On the Fairness of Disentangled Representations Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem
Compiler Auto-Vectorization with Imitation Learning Charith Mendis, Cambridge Yang, Yewen Pu, Dr.Saman Amarasinghe, Michael Carbin
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation Runzhe Yang, Xingyuan Sun, Karthik Narasimhan
Exact Gaussian Processes on a Million Data Points Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
Learning Compositional Neural Programs with Recursive Tree Search and Planning Thomas PIERROT, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric Nirandika Wanigasekara, Christina Yu
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations Debraj Basu, Deepesh Data, Can Karakus, Suhas Diggavi
Likelihood Ratios for Out-of-Distribution Detection Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, Balaji Lakshminarayanan
Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole
A Self Validation Network for Object-Level Human Attention Estimation Zehua Zhang, Chen Yu, David Crandall
Model Selection for Contextual Bandits Dylan J. Foster, Akshay Krishnamurthy, Haipeng Luo
Sliced Gromov-Wasserstein Vayer Titouan, Rémi Flamary, Nicolas Courty, Romain Tavenard, Laetitia Chapel
Towards Practical Alternating Least-Squares for CCA Zhiqiang Xu, Ping Li
Deep Leakage from Gradients Ligeng Zhu, Zhijian Liu, Song Han
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness Fanny Yang, Zuowen Wang, Christina Heinze-Deml
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks Spencer Frei, Yuan Cao, Quanquan Gu
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm Amir-massoud Farahmand
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors Gauri Jagatap, Chinmay Hegde
Planning with Goal-Conditioned Policies Soroush Nasiriany, Vitchyr Pong, Steven Lin, Sergey Levine
Don't take it lightly: Phasing optical random projections with unknown operators Sidharth Gupta, Remi Gribonval, Laurent Daudet, Ivan Dokmanić
Generating Diverse High-Fidelity Images with VQ-VAE-2 Ali Razavi, Aaron van den Oord, Oriol Vinyals
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs Pedro Mercado, Francesco Tudisco, Matthias Hein
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis Yingying Li, Xin Chen, Na Li
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption Wei Ma, George H. Chen
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brébisson, Yoshua Bengio, Aaron C. Courville
Offline Contextual Bandits with High Probability Fairness Guarantees Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain) Mariya Toneva, Leila Wehbe
Function-Space Distributions over Kernels Gregory Benton, Wesley J. Maddox, Jayson Salkey, Julio Albinati, Andrew Gordon Wilson
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
Compositional Plan Vectors Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine
Locally Private Learning without Interaction Requires Separation Amit Daniely, Vitaly Feldman
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences Ehsan Amid, Manfred K. K. Warmuth, Rohan Anil, Tomer Koren
Computational Separations between Sampling and Optimization Kunal Talwar
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks Ganlin Song, Zhou Fan, John Lafferty
Learning to Optimize in Swarms Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
On Human-Aligned Risk Minimization Liu Leqi, Adarsh Prasad, Pradeep K. Ravikumar
Semi-Parametric Efficient Policy Learning with Continuous Actions Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis
Multi-task Learning for Aggregated Data using Gaussian Processes Fariba Yousefi, Michael T. Smith, Mauricio Álvarez
Minimal Variance Sampling in Stochastic Gradient Boosting Bulat Ibragimov, Gleb Gusev
Beyond the Single Neuron Convex Barrier for Neural Network Certification Gagandeep Singh, Rupanshu Ganvir, Markus Püschel, Martin Vechev
An Algorithm to Learn Polytree Networks with Hidden Nodes Firoozeh Sepehr, Donatello Materassi
Efficiently Learning Fourier Sparse Set Functions Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
Invariance and identifiability issues for word embeddings Rachel Carrington, Karthik Bharath, Simon Preston
Generalization Error Analysis of Quantized Compressive Learning Xiaoyun Li, Ping Li
Multi-Criteria Dimensionality Reduction with Applications to Fairness Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H. Morgenstern, Santosh Vempala
Efficient Rematerialization for Deep Networks Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
Understanding Sparse JL for Feature Hashing Meena Jagadeesan
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen
Flexible Modeling of Diversity with Strongly Log-Concave Distributions Joshua Robinson, Suvrit Sra, Stefanie Jegelka
Momentum-Based Variance Reduction in Non-Convex SGD Ashok Cutkosky, Francesco Orabona
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning Ben Eysenbach, Russ R. Salakhutdinov, Sergey Levine
Can Unconditional Language Models Recover Arbitrary Sentences? Nishant Subramani, Samuel Bowman, Kyunghyun Cho
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness Xueru Zhang, Mohammadmahdi Khaliligarekani, Cem Tekin, mingyan liu
Faster width-dependent algorithm for mixed packing and covering LPs Digvijay Boob, Saurabh Sawlani, Di Wang
Flattening a Hierarchical Clustering through Active Learning Fabio Vitale, Anand Rajagopalan, Claudio Gentile
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging Matthieu SIMEONI, Sepand Kashani, Paul Hurley, Martin Vetterli
Certifying Geometric Robustness of Neural Networks Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, Martin Vechev
Goal-conditioned Imitation Learning Yiming Ding, Carlos Florensa, Pieter Abbeel, Mariano Phielipp
Robust exploration in linear quadratic reinforcement learning Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
Input-Output Equivalence of Unitary and Contractive RNNs Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher
Hamiltonian Neural Networks Samuel Greydanus, Misko Dzamba, Jason Yosinski
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B. Grosse, Joern-Henrik Jacobsen
Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks Dina Obeid, Hugo Ramambason, Cengiz Pehlevan
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology Nima Dehmamy, Albert-Laszlo Barabasi, Rose Yu
Multiple Futures Prediction Charlie Tang, Russ R. Salakhutdinov
Explicitly disentangling image content from translation and rotation with spatial-VAE Tristan Bepler, Ellen Zhong, Kotaro Kelley, Edward Brignole, Bonnie Berger
Power analysis of knockoff filters for correlated designs Jingbo Liu, Philippe Rigollet
A Kernel Loss for Solving the Bellman Equation Yihao Feng, Lihong Li, Qiang Liu
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing Jonas W. Mueller, Vasilis Syrgkanis, Matt Taddy
Differential Privacy Has Disparate Impact on Model Accuracy Eugene Bagdasaryan, Omid Poursaeed, Vitaly Shmatikov
Riemannian batch normalization for SPD neural networks Daniel Brooks, Olivier Schwander, Frederic Barbaresco, Jean-Yves Schneider, Matthieu Cord
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity Aria Wang, Michael Tarr, Leila Wehbe
Stacked Capsule Autoencoders Adam Kosiorek, Sara Sabour, Yee Whye Teh, Geoffrey E. Hinton
Learning Reward Machines for Partially Observable Reinforcement Learning Rodrigo Toro Icarte, Ethan Waldie, Toryn Klassen, Rick Valenzano, Margarita Castro, Sheila McIlraith
Learning Representations by Maximizing Mutual Information Across Views Philip Bachman, R Devon Hjelm, William Buchwalter
Amortized Bethe Free Energy Minimization for Learning MRFs Sam Wiseman, Yoon Kim
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Aaron Voelker, Ivana Kajić, Chris Eliasmith
Exact Combinatorial Optimization with Graph Convolutional Neural Networks Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Fast structure learning with modular regularization Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan
Wasserstein Dependency Measure for Representation Learning Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet
TAB-VCR: Tags and Attributes based VCR Baselines Jingxiang Lin, Unnat Jain, Alexander Schwing
Universality and individuality in neural dynamics across large populations of recurrent networks Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, David Sussillo
End-to-End Learning on 3D Protein Structure for Interface Prediction Raphael Townshend, Rishi Bedi, Patricia Suriana, Ron Dror
A Family of Robust Stochastic Operators for Reinforcement Learning Yingdong Lu, Mark Squillante, Chai Wah Wu
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song
Inherent Tradeoffs in Learning Fair Representations Han Zhao, Geoff Gordon
Are deep ResNets provably better than linear predictors? Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, David Sussillo
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models Yuge Shi, Siddharth N, Brooks Paige, Philip Torr
Gradient-based Adaptive Markov Chain Monte Carlo Michalis Titsias, Petros Dellaportas
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer
Imitation-Projected Programmatic Reinforcement Learning Abhinav Verma, Hoang Le, Yisong Yue, Swarat Chaudhuri
Learning Data Manipulation for Augmentation and Weighting Zhiting Hu, Bowen Tan, Russ R. Salakhutdinov, Tom M. Mitchell, Eric P. Xing
Exploring Algorithmic Fairness in Robust Graph Covering Problems Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe
Abstraction based Output Range Analysis for Neural Networks Pavithra Prabhakar, Zahra Rahimi Afzal
Space and Time Efficient Kernel Density Estimation in High Dimensions Arturs Backurs, Piotr Indyk, Tal Wagner
PIDForest: Anomaly Detection via Partial Identification Parikshit Gopalan, Vatsal Sharan, Udi Wieder
Generative Models for Graph-Based Protein Design John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
The Geometry of Deep Networks: Power Diagram Subdivision Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk
Approximate Feature Collisions in Neural Nets Ke Li, Tianhao Zhang, Jitendra Malik
Ease-of-Teaching and Language Structure from Emergent Communication Fushan Li, Michael Bowling
Generalization in multitask deep neural classifiers: a statistical physics approach Anthony Ndirango, Tyler Lee
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann
On Relating Explanations and Adversarial Examples Alexey Ignatiev, Nina Narodytska, Joao Marques-Silva
On the equivalence between graph isomorphism testing and function approximation with GNNs Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks Hosein Hasani, Mahdieh Soleymani, Hamid Aghajan
Self-attention with Functional Time Representation Learning Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling Ping Li, Xiaoyun Li, Cun-Hui Zhang
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data Mohammad Reza Keshtkaran, Chethan Pandarinath
Mixtape: Breaking the Softmax Bottleneck Efficiently Zhilin Yang, Thang Luong, Russ R. Salakhutdinov, Quoc V. Le