NIPS 2015 Proceedings

Advances in Neural Information Processing Systems 28 (NIPS 2015)

The papers below appear in Advances in Neural Information Processing Systems 28 edited by C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett.
They are proceedings from the conference, "Neural Information Processing Systems 2015."

Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadresh Shah, Denny Zhou
Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson
Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Theodoros Tsiligkaridis, Keith Forsythe
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Logarithmic Time Online Multiclass prediction Anna E. Choromanska, John Langford
Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes
Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim
Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements Qinqing Zheng, John Lafferty
Smooth Interactive Submodular Set Cover Bryan D. He, Yisong Yue
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
On the Pseudo-Dimension of Nearly Optimal Auctions Jamie H. Morgenstern, Tim Roughgarden
Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti
Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov
Where are they looking? Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
The Pareto Regret Frontier for Bandits Tor Lattimore
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari, Daniel Reichman, Ofer Zeitouni
Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang, Wei Wang, Liang Wang
Bounding errors of Expectation-Propagation Guillaume P. Dehaene, Simon Barthelmé
A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
Texture Synthesis Using Convolutional Neural Networks Leon Gatys, Alexander S. Ecker, Matthias Bethge
Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi, Mehrdad Mahdavi, Alex Schwing
Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke
Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele
Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen
Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass
Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin
Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins
Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye
3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
A Reduced-Dimension fMRI Shared Response Model Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge
Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu, Prashant Doshi
Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi
Deep Knowledge Tracing Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein
Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg
Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri
Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
Attention-Based Models for Speech Recognition Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio
Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
Online F-Measure Optimization Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen, Pekka Parviainen
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring Gunwoong Park, Garvesh Raskutti
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala
Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen
Black-box optimization of noisy functions with unknown smoothness Jean-Bastien grill, Michal Valko, Remi Munos, Remi Munos
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters Emmanuel Abbe, Colin Sandon
Deep learning with Elastic Averaging SGD Sixin Zhang, Anna E. Choromanska, Yann LeCun
Monotone k-Submodular Function Maximization with Size Constraints Naoto Ohsaka, Yuichi Yoshida
Active Learning from Weak and Strong Labelers Chicheng Zhang, Kamalika Chaudhuri
On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu, Ivor Tsang
Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar
Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam, Suchi Saria
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu, Huan Xu
Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney
Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava, Elad Hazan, Shie Mannor
Convolutional spike-triggered covariance analysis for neural subunit models Anqi Wu, Il Memming Park, Jonathan W. Pillow
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi, Takafumi Kanamori
Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani
Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma, Yuichi Yoshida
Bidirectional Recurrent Neural Networks as Generative Models Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha T. Karhunen
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks Minhyung Cho, Chandra Dhir, Jaehyung Lee
Large-scale probabilistic predictors with and without guarantees of validity Vladimir Vovk, Ivan Petej, Valentina Fedorova
Shepard Convolutional Neural Networks Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun
Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Rie Johnson, Tong Zhang
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto, Radu Marinescu, Adi Botea
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling Ming Liang, Xiaolin Hu, Bo Zhang
Bounding the Cost of Search-Based Lifted Inference David B. Smith, Vibhav G. Gogate
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton
Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari
Unsupervised Learning by Program Synthesis Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan, Sophie Denève
Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alex J. Smola, Anima Anandkumar
Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh
Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs Wei Cao, Jian Li, Yufei Tao, Zhize Li
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions Sebastian Bitzer, Stefan Kiebel
Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar
Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Human Memory Search as Initial-Visit Emitting Random Walk Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, ming yuan
Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier
Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha
Alternating Minimization for Regression Problems with Vector-valued Outputs Prateek Jain, Ambuj Tewari
Learning both Weights and Connections for Efficient Neural Network Song Han, Jeff Pool, John Tran, William Dally
Optimal Rates for Random Fourier Features Bharath Sriperumbudur, Zoltan Szabo
The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh
Efficient Output Kernel Learning for Multiple Tasks Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William T. Stephenson, Erik Sudderth
Variational Consensus Monte Carlo Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma Murat A. Erdogdu
Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
Learning to Linearize Under Uncertainty Ross Goroshin, Michael F. Mathieu, Yann LeCun
Finite-Time Analysis of Projected Langevin Monte Carlo Sebastien Bubeck, Ronen Eldan, Joseph Lehec
Deep Visual Analogy-Making Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade, Florent Krzakala, Lenka Zdeborová
Online Learning with Adversarial Delays Kent Quanrud, Daniel Khashabi
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang, Jieping Ye
Minimum Weight Perfect Matching via Blossom Belief Propagation Sung-Soo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong
Lifted Symmetry Detection and Breaking for MAP Inference Timothy Kopp, Parag Singla, Henry Kautz
Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Discriminative Robust Transformation Learning Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank
Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl
Scalable Semi-Supervised Aggregation of Classifiers Akshay Balsubramani, Yoav Freund
Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari
Private Graphon Estimation for Sparse Graphs Christian Borgs, Jennifer Chayes, Adam Smith
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun, Dhruv Batra
Fast Second Order Stochastic Backpropagation for Variational Inference Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla
Fast Bidirectional Probability Estimation in Markov Models Siddhartha Banerjee, Peter Lofgren
Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher III, Alexander T. Ihler
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J. Giordano, Tamara Broderick, Michael I. Jordan
Combinatorial Cascading Bandits Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari
Policy Gradient for Coherent Risk Measures Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
Fast Rates for Exp-concave Empirical Risk Minimization Tomer Koren, Kfir Levy
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
Equilibrated adaptive learning rates for non-convex optimization Yann Dauphin, Harm de Vries, Yoshua Bengio
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow, Aviv Tamar, Shie Mannor, Marco Pavone
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care Sorathan Chaturapruek, John C. Duchi, Christopher Ré
Lifelong Learning with Non-i.i.d. Tasks Anastasia Pentina, Christoph H. Lampert
Optimal Linear Estimation under Unknown Nonlinear Transform Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
Learning with Group Invariant Features: A Kernel Perspective. Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees Xinyang Yi, Constantine Caramanis
Distributionally Robust Logistic Regression Soroosh Shafieezadeh-Abadeh, Peyman Mohajerin Esfahani, Daniel Kuhn
Adaptive Stochastic Optimization: From Sets to Paths Zhan Wei Lim, David Hsu, Wee Sun Lee
Beyond Convexity: Stochastic Quasi-Convex Optimization Elad Hazan, Kfir Levy, Shai Shalev-Shwartz
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding Yuval Harel, Ron Meir, Manfred Opper
Sum-of-Squares Lower Bounds for Sparse PCA Tengyu Ma, Avi Wigderson
Max-Margin Majority Voting for Learning from Crowds TIAN TIAN, Jun Zhu
Learning with Incremental Iterative Regularization Lorenzo Rosasco, Silvia Villa
Halting in Random Walk Kernels Mahito Sugiyama, Karsten Borgwardt
MCMC for Variationally Sparse Gaussian Processes James Hensman, Alexander G. Matthews, Maurizio Filippone, Zoubin Ghahramani
Less is More: Nyström Computational Regularization Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
Infinite Factorial Dynamical Model Isabel Valera, Francisco Ruiz, Lennart Svensson, Fernando Perez-Cruz
Regularization Path of Cross-Validation Error Lower Bounds Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments Dane S. Corneil, Wulfram Gerstner
Teaching Machines to Read and Comprehend Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions Jonas W. Mueller, Tommi Jaakkola
When are Kalman-Filter Restless Bandits Indexable? Christopher R. Dance, Tomi Silander
Segregated Graphs and Marginals of Chain Graph Models Ilya Shpitser
Efficient Non-greedy Optimization of Decision Trees Mohammad Norouzi, Maxwell Collins, Matthew A. Johnson, David J. Fleet, Pushmeet Kohli
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process Ye Wang, David B. Dunson
Inverse Reinforcement Learning with Locally Consistent Reward Functions Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Communication Complexity of Distributed Convex Learning and Optimization Yossi Arjevani, Ohad Shamir
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng
Subset Selection by Pareto Optimization Chao Qian, Yang Yu, Zhi-Hua Zhou
On the Accuracy of Self-Normalized Log-Linear Models Jacob Andreas, Maxim Rabinovich, Michael I. Jordan, Dan Klein
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring Junpei Komiyama, Junya Honda, Hiroshi Nakagawa
Is Approval Voting Optimal Given Approval Votes? Ariel D. Procaccia, Nisarg Shah
Regressive Virtual Metric Learning Michaël Perrot, Amaury Habrard
Analysis of Robust PCA via Local Incoherence Huishuai Zhang, Yi Zhou, Yingbin Liang
Learning to Transduce with Unbounded Memory Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Max-Margin Deep Generative Models Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang
Spherical Random Features for Polynomial Kernels Jeffrey Pennington, Felix Yu, Sanjiv Kumar
Rectified Factor Networks Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter
Learning Bayesian Networks with Thousands of Variables Mauro Scanagatta, Cassio P. de Campos, Giorgio Corani, Marco Zaffalon
Matrix Completion Under Monotonic Single Index Models Ravi Sastry Ganti, Laura Balzano, Rebecca Willett
Visalogy: Answering Visual Analogy Questions Fereshteh Sadeghi, C. Lawrence Zitnick, Ali Farhadi
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models Juho Lee, Seungjin Choi
Streaming Min-max Hypergraph Partitioning Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
Collaboratively Learning Preferences from Ordinal Data Sewoong Oh, Kiran K. Thekumparampil, Jiaming Xu
Biologically Inspired Dynamic Textures for Probing Motion Perception Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet, Gabriel Peyré
Generative Image Modeling Using Spatial LSTMs Lucas Theis, Matthias Bethge
Robust PCA with compressed data Wooseok Ha, Rina Foygel Barber
Sampling from Probabilistic Submodular Models Alkis Gotovos, Hamed Hassani, Andreas Krause
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Mehrdad Farajtabar, Yichen Wang, Manuel Gomez-Rodriguez, Shuang Li, Hongyuan Zha, Le Song
Supervised Learning for Dynamical System Learning Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
Regret-Based Pruning in Extensive-Form Games Noam Brown, Tuomas Sandholm
Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper P. Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton
Learning to Segment Object Candidates Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar
GP Kernels for Cross-Spectrum Analysis Kyle R. Ulrich, David E. Carlson, Kafui Dzirasa, Lawrence Carin
Secure Multi-party Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Spatial Transformer Networks Max Jaderberg, Karen Simonyan, Andrew Zisserman, koray kavukcuoglu
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms Yunwen Lei, Urun Dogan, Alexander Binder, Marius Kloft
High-dimensional neural spike train analysis with generalized count linear dynamical systems YUANJUN GAO, Lars Busing, Krishna V. Shenoy, John P. Cunningham
Learning with a Wasserstein Loss Charlie Frogner, Chiyuan Zhang, Hossein Mobahi, Mauricio Araya, Tomaso A. Poggio
b-bit Marginal Regression Martin Slawski, Ping Li
Natural Neural Networks Guillaume Desjardins, Karen Simonyan, Razvan Pascanu, koray kavukcuoglu
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference Ted Meeds, Max Welling
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing Tom Goldstein, Min Li, Xiaoming Yuan
On some provably correct cases of variational inference for topic models Pranjal Awasthi, Andrej Risteski
Collaborative Filtering with Graph Information: Consistency and Scalable Methods Nikhil Rao, Hsiang-Fu Yu, Pradeep K. Ravikumar, Inderjit S. Dhillon
Combinatorial Bandits Revisited Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, marc lelarge
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning Shakir Mohamed, Danilo Jimenez Rezende
A Structural Smoothing Framework For Robust Graph Comparison Pinar Yanardag, S.V.N. Vishwanathan
Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky, Ananda Theertha Suresh
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama
A hybrid sampler for Poisson-Kingman mixture models Maria Lomeli, Stefano Favaro, Yee Whye Teh
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching Xiao Li, Kannan Ramchandran
Local Smoothness in Variance Reduced Optimization Daniel Vainsencher, Han Liu, Tong Zhang
Saliency, Scale and Information: Towards a Unifying Theory Shafin Rahman, Neil Bruce
Fighting Bandits with a New Kind of Smoothness Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs Vidyashankar Sivakumar, Arindam Banerjee, Pradeep K. Ravikumar
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction Rakesh Shivanna, Bibaswan K. Chatterjee, Raman Sankaran, Chiranjib Bhattacharyya, Francis Bach
Convolutional Networks on Graphs for Learning Molecular Fingerprints David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes
Tractable Learning for Complex Probability Queries Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck
StopWasting My Gradients: Practical SVRG Reza Harikandeh, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction Been Kim, Julie A. Shah, Finale Doshi-Velez
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks Cengiz Pehlevan, Dmitri Chklovskii
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators Changyou Chen, Nan Ding, Lawrence Carin
Learning structured densities via infinite dimensional exponential families Siqi Sun, mladen kolar, Jinbo Xu
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu
Variance Reduced Stochastic Gradient Descent with Neighbors Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, Brian McWilliams
Sample Efficient Path Integral Control under Uncertainty Yunpeng Pan, Evangelos Theodorou, Michail Kontitsis
Stochastic Expectation Propagation Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
Exactness of Approximate MAP Inference in Continuous MRFs Nicholas Ruozzi
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients Bo Xie, Yingyu Liang, Le Song
Generalization in Adaptive Data Analysis and Holdout Reuse Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents Mithun Chakraborty, Sanmay Das
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
Training Very Deep Networks Rupesh K. Srivastava, Klaus Greff, Juergen Schmidhuber
Bayesian Active Model Selection with an Application to Automated Audiometry Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham
Particle Gibbs for Infinite Hidden Markov Models Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani
Learning spatiotemporal trajectories from manifold-valued longitudinal data Jean-Baptiste SCHIRATTI, Stéphanie ALLASSONNIERE, Olivier Colliot, Stanley DURRLEMAN
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making Koosha Khalvati, Rajesh P. Rao
Path-SGD: Path-Normalized Optimization in Deep Neural Networks Behnam Neyshabur, Ruslan R. Salakhutdinov, Nati Srebro
On the consistency theory of high dimensional variable screening Xiangyu Wang, Chenlei Leng, David B. Dunson
End-To-End Memory Networks Sainbayar Sukhbaatar, arthur szlam, Jason Weston, Rob Fergus
Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams
Online Gradient Boosting Alina Beygelzimer, Elad Hazan, Satyen Kale, Haipeng Luo
Deep Temporal Sigmoid Belief Networks for Sequence Modeling Zhe Gan, Chunyuan Li, Ricardo Henao, David E. Carlson, Lawrence Carin
Recognizing retinal ganglion cells in the dark Emile Richard, Georges A. Goetz, E. J. Chichilnisky
A Theory of Decision Making Under Dynamic Context Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen
A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams
Hidden Technical Debt in Machine Learning Systems D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
Local Causal Discovery of Direct Causes and Effects Tian Gao, Qiang Ji
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
Revenue Optimization against Strategic Buyers Mehryar Mohri, Andres Munoz
Deep Convolutional Inverse Graphics Network Tejas D. Kulkarni, William F. Whitney, Pushmeet Kohli, Josh Tenenbaum
Sparse and Low-Rank Tensor Decomposition Parikshit Shah, Nikhil Rao, Gongguo Tang
Minimax Time Series Prediction Wouter M. Koolen, Alan Malek, Peter L. Bartlett, Yasin Abbasi
Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt
Variational Dropout and the Local Reparameterization Trick Avrim Blum, Nika Haghtalab, Ariel D. Procaccia
Sample Complexity of Learning Mahalanobis Distance Metrics Nakul Verma, Kristin Branson
Learning Wake-Sleep Recurrent Attention Models Jimmy Ba, Ruslan R. Salakhutdinov, Roger B. Grosse, Brendan J. Frey
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso Eunho Yang, Aurelie C. Lozano
Testing Closeness With Unequal Sized Samples Bhaswar Bhattacharya, Gregory Valiant
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach Wenye Li
Neural Adaptive Sequential Monte Carlo Shixiang Gu, Zoubin Ghahramani, Richard E. Turner
Local Expectation Gradients for Black Box Variational Inference Michalis Titsias, Miguel Lázaro-Gredilla
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex J. Smola
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning Kevin G. Jamieson, Lalit Jain, Chris Fernandez, Nicholas J. Glattard, Rob Nowak
Super-Resolution Off the Grid Qingqing Huang, Sham M. Kakade
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré, Christopher Ré
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors Dan Rosenbaum, Yair Weiss
Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
Associative Memory via a Sparse Recovery Model Arya Mazumdar, Ankit Singh Rawat
Robust Spectral Inference for Joint Stochastic Matrix Factorization Moontae Lee, David Bindel, David Mimno
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms Rasmus Kyng, Anup Rao, Sushant Sachdeva
Adversarial Prediction Games for Multivariate Losses Hong Wang, Wei Xing, Kaiser Asif, Brian Ziebart
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization Xiangru Lian, Yijun Huang, Yuncheng Li, Ji Liu
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images Manuel Watter, Jost Springenberg, Joschka Boedecker, Martin Riedmiller
Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Softstar: Heuristic-Guided Probabilistic Inference Mathew Monfort, Brenden M. Lake, Brenden M. Lake, Brian Ziebart, Patrick Lucey, Josh Tenenbaum
Grammar as a Foreign Language Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices Martin Slawski, Ping Li, Matthias Hein
Winner-Take-All Autoencoders Alireza Makhzani, Brendan J. Frey
Deep Poisson Factor Modeling Ricardo Henao, Zhe Gan, James Lu, Lawrence Carin
Bayesian Optimization with Exponential Convergence Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning Christoph Dann, Emma Brunskill
Learning with Relaxed Supervision Jacob Steinhardt, Percy S. Liang
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's Vitaly Feldman, Will Perkins, Santosh Vempala
Accelerated Mirror Descent in Continuous and Discrete Time Walid Krichene, Alexandre Bayen, Peter L. Bartlett
The Human Kernel Andrew G. Wilson, Christoph Dann, Chris Lucas, Eric P. Xing
Action-Conditional Video Prediction using Deep Networks in Atari Games Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, Satinder Singh
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA James R. Voss, Mikhail Belkin, Luis Rademacher
Distributed Submodular Cover: Succinctly Summarizing Massive Data Baharan Mirzasoleiman, Amin Karbasi, Ashwinkumar Badanidiyuru, Andreas Krause
Community Detection via Measure Space Embedding Mark Kozdoba, Shie Mannor
Basis refinement strategies for linear value function approximation in MDPs Gheorghe Comanici, Doina Precup, Prakash Panangaden
Structured Estimation with Atomic Norms: General Bounds and Applications Sheng Chen, Arindam Banerjee
A Complete Recipe for Stochastic Gradient MCMC Yi-An Ma, Tianqi Chen, Emily Fox
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff Ofer Dekel, Ronen Eldan, Tomer Koren
Online Prediction at the Limit of Zero Temperature Mark Herbster, Stephen Pasteris, Shaona Ghosh
Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Tim Lillicrap, Tom Erez, Yuval Tassa
Exploring Models and Data for Image Question Answering Mengye Ren, Ryan Kiros, Richard Zemel
Efficient and Robust Automated Machine Learning Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, Frank Hutter
Preconditioned Spectral Descent for Deep Learning David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher
A Recurrent Latent Variable Model for Sequential Data Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C. Courville, Yoshua Bengio
Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Juergen Schmidhuber
Reflection, Refraction, and Hamiltonian Monte Carlo Hadi Mohasel Afshar, Justin Domke
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels Purnamrita Sarkar, Deepayan Chakrabarti, peter j. bickel
Nearly Optimal Private LASSO Kunal Talwar, Abhradeep Thakurta, Li Zhang
Convergence Analysis of Prediction Markets via Randomized Subspace Descent Rafael Frongillo, Mark D. Reid
The Poisson Gamma Belief Network Mingyuan Zhou, Yulai Cong, Bo Chen
Convergence rates of sub-sampled Newton methods Murat A. Erdogdu, Andrea Montanari
No-Regret Learning in Bayesian Games Jason Hartline, Vasilis Syrgkanis, Eva Tardos
Statistical Topological Data Analysis - A Kernel Perspective Roland Kwitt, Stefan Huber, Marc Niethammer, Weili Lin, Ulrich Bauer
Semi-supervised Sequence Learning Andrew M. Dai, Quoc V. Le
Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width Christopher M. De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm Qinqing Zheng, Ryota Tomioka
Sample Complexity Bounds for Iterative Stochastic Policy Optimization Marin Kobilarov
BinaryConnect: Training Deep Neural Networks with binary weights during propagations Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Interactive Control of Diverse Complex Characters with Neural Networks Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popovic, Emanuel V. Todorov
Submodular Hamming Metrics Jennifer A. Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
A Universal Primal-Dual Convex Optimization Framework Alp Yurtsever, Quoc Tran Dinh, Volkan Cevher
Learning From Small Samples: An Analysis of Simple Decision Heuristics Özgür Şimşek, Marcus Buckmann
Explore no more: Improved high-probability regret bounds for non-stochastic bandits Gergely Neu
Fast and Memory Optimal Low-Rank Matrix Approximation Se-Young Yun, marc lelarge, Alexandre Proutiere
Learnability of Influence in Networks Harikrishna Narasimhan, David C. Parkes, Yaron Singer
Learning Causal Graphs with Small Interventions Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath
Information-theoretic lower bounds for convex optimization with erroneous oracles Yaron Singer, Jan Vondrak
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings Piyush Rai, Changwei Hu, Ricardo Henao, Lawrence Carin
The Self-Normalized Estimator for Counterfactual Learning Adith Swaminathan, Thorsten Joachims
Fast Lifted MAP Inference via Partitioning Somdeb Sarkhel, Parag Singla, Vibhav G. Gogate
Data Generation as Sequential Decision Making Philip Bachman, Doina Precup
On Elicitation Complexity Rafael Frongillo, Ian Kash
Decomposition Bounds for Marginal MAP Wei Ping, Qiang Liu, Alexander T. Ihler
Discrete Rényi Classifiers Meisam Razaviyayn, Farzan Farnia, David Tse
A class of network models recoverable by spectral clustering Yali Wan, Marina Meila
Skip-Thought Vectors Ryan Kiros, Yukun Zhu, Ruslan R. Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Rate-Agnostic (Causal) Structure Learning Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric Vivien Seguy, Marco Cuturi
Consistent Multilabel Classification Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions Amar Shah, Zoubin Ghahramani
Cornering Stationary and Restless Mixing Bandits with Remix-UCB Julien Audiffren, Liva Ralaivola
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Danilo Bzdok, Michael Eickenberg, Olivier Grisel, Bertrand Thirion, Gael Varoquaux
Gaussian Process Random Fields David Moore, Stuart J. Russell
M-Statistic for Kernel Change-Point Detection Shuang Li, Yao Xie, Hanjun Dai, Le Song
Adaptive Online Learning Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
A Universal Catalyst for First-Order Optimization Hongzhou Lin, Julien Mairal, Zaid Harchaoui
Inference for determinantal point processes without spectral knowledge Rémi Bardenet, Michalis Titsias
Kullback-Leibler Proximal Variational Inference Mohammad E. Khan, Pierre Baque, François Fleuret, Pascal Fua
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization Niao He, Zaid Harchaoui
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements CHRISTOS THRAMPOULIDIS, Ehsan Abbasi, Babak Hassibi
From random walks to distances on unweighted graphs Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola
Bayesian dark knowledge Anoop Korattikara Balan, Vivek Rathod, Kevin P. Murphy, Max Welling
Matrix Completion with Noisy Side Information Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit S. Dhillon
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation Scott Linderman, Matthew Johnson, Ryan P. Adams
On-the-Job Learning with Bayesian Decision Theory Keenon Werling, Arun Tejasvi Chaganty, Percy S. Liang, Christopher D. Manning
Calibrated Structured Prediction Volodymyr Kuleshov, Percy S. Liang
Learning Structured Output Representation using Deep Conditional Generative Models Kihyuk Sohn, Honglak Lee, Xinchen Yan
Time-Sensitive Recommendation From Recurrent User Activities Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels Felipe Tobar, Thang D. Bui, Richard E. Turner
A Market Framework for Eliciting Private Data Bo Waggoner, Rafael Frongillo, Jacob D. Abernethy
Lifted Inference Rules With Constraints Happy Mittal, Anuj Mahajan, Vibhav G. Gogate, Parag Singla
Gradient Estimation Using Stochastic Computation Graphs John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Model-Based Relative Entropy Stochastic Search Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
Semi-supervised Learning with Ladder Networks Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko
Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans
Copula variational inference Dustin Tran, David Blei, Edo M. Airoldi
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin, H. Sebastian Seung
A Dual Augmented Block Minimization Framework for Learning with Limited Memory Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
Optimal Testing for Properties of Distributions Jayadev Acharya, Constantinos Daskalakis, Gautam C. Kamath
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg
Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet
Latent Bayesian melding for integrating individual and population models Mingjun Zhong, Nigel Goddard, Charles Sutton