Advances in Neural Information Processing Systems 27

NIPS 2014

Edited by Z. Ghahramani and M. Welling and C. Cortes and N. Lawrence and K.Q. Weinberger

ISBN: 9781510800410

411 papers

From Stochastic Mixability to Fast Rates Nishant A Mehta, Robert C. Williamson
Active Regression by Stratification Sivan Sabato, Remi Munos
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks Cristina Savin, Sophie Denève
Biclustering Using Message Passing Luke O'Connor, Soheil Feizi
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A Machado, John P. Cunningham, Liam Paninski
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
Gaussian Process Volatility Model Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
Distributed Estimation, Information Loss and Exponential Families Qiang Liu, Alex Ihler
Cone-Constrained Principal Component Analysis Yash Deshpande, Andrea Montanari, Emile Richard
Dynamic Rank Factor Model for Text Streams Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin
Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu, Michal Valko
Magnitude-sensitive preference formation` Nisheeth Srivastava, Ed Vul, Paul R. Schrater
Learning convolution filters for inverse covariance estimation of neural network connectivity George Mohler
Sparse PCA via Covariance Thresholding Yash Deshpande, Andrea Montanari
Online Optimization for Max-Norm Regularization Jie Shen, Huan Xu, Ping Li
Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau
Dependent nonparametric trees for dynamic hierarchical clustering Kumar Avinava Dubey, Qirong Ho, Sinead A Williamson, Eric P Xing
Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri
Distance-Based Network Recovery under Feature Correlation David Adametz, Volker Roth
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers Bruno Conejo, Nikos Komodakis, Sebastien Leprince, Jean Philippe Avouac
A Complete Variational Tracker Ryan D Turner, Steven Bottone, Bhargav Avasarala
Optimal prior-dependent neural population codes under shared input noise Agnieszka Grabska-Barwinska, Jonathan W Pillow
Conditional Swap Regret and Conditional Correlated Equilibrium Mehryar Mohri, Scott Yang
Extracting Latent Structure From Multiple Interacting Neural Populations Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
Near-optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter
Difference of Convex Functions Programming for Reinforcement Learning Bilal Piot, Matthieu Geist, Olivier Pietquin
SerialRank: Spectral Ranking using Seriation Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning Marek Petrik, Dharmashankar Subramanian
Covariance shrinkage for autocorrelated data Daniel Bartz, Klaus-Robert Müller
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang
Exact Post Model Selection Inference for Marginal Screening Jason Lee, Jonathan E Taylor
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon
Sequential Monte Carlo for Graphical Models Christian Andersson Naesseth, Fredrik Lindsten, Thomas B Schön
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S Shawe-Taylor
Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
Extreme bandits Alexandra Carpentier, Michal Valko
Low Rank Approximation Lower Bounds in Row-Update Streams David Woodruff
Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L Bartlett
Probabilistic low-rank matrix completion on finite alphabets Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan, Judea Pearl
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A Gibson, Eric P Xing
Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Learning Deep Features for Scene Recognition using Places Database Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
A Framework for Testing Identifiability of Bayesian Models of Perception Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus
Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos
Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado P van Hasselt, Richard S. Sutton
Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra
Provable Submodular Minimization using Wolfe's Algorithm Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
Top Rank Optimization in Linear Time Nan Li, Rong Jin, Zhi-Hua Zhou
On Multiplicative Multitask Feature Learning Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
Flexible Transfer Learning under Support and Model Shift Xuezhi Wang, Jeff Schneider
Controlling privacy in recommender systems Yu Xin, Tommi Jaakkola
Deep Networks with Internal Selective Attention through Feedback Connections Marijn F Stollenga, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber
Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Tung, Alexander J Smola
On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
Feature Cross-Substitution in Adversarial Classification Bo Li, Yevgeniy Vorobeychik
A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo, Robert E. Schapire
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis Titsias RC AUEB, Christopher Yau
Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh
Feedforward Learning of Mixture Models Matthew Lawlor, Steven W Zucker
Causal Strategic Inference in Networked Microfinance Economies Mohammad T Irfan, Luis E. Ortiz
Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
Recovery of Coherent Data via Low-Rank Dictionary Pursuit Guangcan Liu, Ping Li
On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg, Tengyu Ma, Huy Nguyen
Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena, Munther Dahleh
Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y Zheng, Franco Pestilli, Ariel Rokem
Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
Stochastic Proximal Gradient Descent with Acceleration Techniques Atsushi Nitanda
Augur: Data-Parallel Probabilistic Modeling Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam C Pocock, Stephen Green, Guy L Steele
An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
Latent Support Measure Machines for Bag-of-Words Data Classification Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada
Deep Learning Face Representation by Joint Identification-Verification Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces Minh Ha Quang, Marco San Biagio, Vittorio Murino
Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
Learning Multiple Tasks in Parallel with a Shared Annotator Haim Cohen, Koby Crammer
large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising Charles Kervrann
Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C Tschantz, Ling Huang, Peter L Bartlett, Anthony D Joseph, J. D. Tygar
Hardness of parameter estimation in graphical models Guy Bresler, David Gamarnik, Devavrat Shah
Decoupled Variational Gaussian Inference Mohammad Emtiyaz Khan
Unsupervised Deep Haar Scattering on Graphs Xu Chen, Xiuyuan Cheng, Stephane Mallat
Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S Dhillon, Pradeep K Ravikumar, Stephen Becker, Peder A. Olsen
Deep Convolutional Neural Network for Image Deconvolution Li Xu, Jimmy SJ Ren, Ce Liu, Jiaya Jia
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang
Improved Multimodal Deep Learning with Variation of Information Kihyuk Sohn, Wenling Shang, Honglak Lee
Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
The Noisy Power Method: A Meta Algorithm with Applications Moritz Hardt, Eric Price
Convex Optimization Procedure for Clustering: Theoretical Revisit Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan
On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
Bandit Convex Optimization: Towards Tight Bounds Elad Hazan, Kfir Levy
Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovich
Clamping Variables and Approximate Inference Adrian Weller, Tony Jebara
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng
Non-convex Robust PCA Praneeth Netrapalli, Niranjan U N, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, koray kavukcuoglu
Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
Elementary Estimators for Graphical Models Eunho Yang, Aurelie C. Lozano, Pradeep K Ravikumar
Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A Shah
Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, Jeff A. Bilmes
Deep Symmetry Networks Robert Gens, Pedro M Domingos
Submodular Attribute Selection for Action Recognition in Video Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P Phillips
Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal, Vibhav G Gogate
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I Jordan
Learning Generative Models with Visual Attention Charlie Tang, Nitish Srivastava, Ruslan Salakhutdinov
Searching for Higgs Boson Decay Modes with Deep Learning Peter J Sadowski, Daniel Whiteson, Pierre Baldi
A provable SVD-based algorithm for learning topics in dominant admixture corpus Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep K Ravikumar, Inderjit S Dhillon
Scalable Non-linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J Telgarsky
New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal, Vibhav G Gogate, Parag Singla
Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
Blossom Tree Graphical Models Zhe Liu, John Lafferty
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
A Wild Bootstrap for Degenerate Kernel Tests Kacper P Chwialkowski, Dino Sejdinovic, Arthur Gretton
Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
Do Convnets Learn Correspondence? Jonathan L Long, Ning Zhang, Trevor Darrell
Optimal Teaching for Limited-Capacity Human Learners Kaustubh R Patil, Xiaojin Zhu, Łukasz Kopeć, Bradley C. Love
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
Time--Data Tradeoffs by Aggressive Smoothing John J Bruer, Joel A Tropp, Volkan Cevher, Stephen Becker
Analysis of Brain States from Multi-Region LFP Time-Series Kyle R Ulrich, David E Carlson, Wenzhao Lian, Jana S Borg, Kafui Dzirasa, Lawrence Carin
The Blinded Bandit: Learning with Adaptive Feedback Ofer Dekel, Elad Hazan, Tomer Koren
How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M Eisner
Fairness in Multi-Agent Sequential Decision-Making Chongjie Zhang, Julie A Shah
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
Learning to Discover Efficient Mathematical Identities Wojciech Zaremba, Karol Kurach, Rob Fergus
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings Sebastian Stober, Daniel J Cameron, Jessica A Grahn
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V Le
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Mehmet Gönen, Adam A Margolin
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks Yanping Huang, Rajesh P. Rao
The Infinite Mixture of Infinite Gaussian Mixtures Halid Z Yerebakan, Bartek Rajwa, Murat Dundar
Low-dimensional models of neural population activity in sensory cortical circuits Evan W Archer, Urs Koster, Jonathan W Pillow, Jakob H. Macke
Shaping Social Activity by Incentivizing Users Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song
Optimizing F-Measures by Cost-Sensitive Classification Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
Efficient Partial Monitoring with Prior Information Hastagiri P Vanchinathan, Gábor Bartók, Andreas Krause
A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
Learning the Learning Rate for Prediction with Expert Advice Wouter M. Koolen, Tim van Erven, Peter Grünwald
Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
Fast Sampling-Based Inference in Balanced Neuronal Networks Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
Design Principles of the Hippocampal Cognitive Map Kimberly L Stachenfeld, Matthew Botvinick, Samuel J Gershman
Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi, Tony Jebara
A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E Gonzalez, Joseph K. Bradley, Michael I Jordan
Stochastic variational inference for hidden Markov models Nicholas Foti, Jason Xu, Dillon Laird, Emily B. Fox
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato, Matthew W Hoffman, Zoubin Ghahramani
A Safe Screening Rule for Sparse Logistic Regression Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems Cong Han Lim, Stephen Wright
Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L Ying, Philip Resnik, Jonathan Chang
Large-scale L-BFGS using MapReduce Weizhu Chen, Zhenghao Wang, Jingren Zhou
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers Mehryar Mohri, Andres Munoz
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations Xianjie Chen, Alan L. Yuille
Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
Repeated Contextual Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
Semi-supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi, Bernd Gärtner, Andreas Krause
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I Jordan
Content-based recommendations with Poisson factorization Prem Gopalan, Laurent Charlin, David Blei
Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
How hard is my MDP?" The distribution-norm to the rescue" Odalric-Ambrym Maillard, Timothy A Mann, Shie Mannor
An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G Gogate
Bayes-Adaptive Simulation-based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh, Volkan Cevher
Metric Learning for Temporal Sequence Alignment Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach
An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
Universal Option Models hengshuai yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
Convolutional Kernel Networks Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid
Coresets for k-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
On the relations of LFPs & Neural Spike Trains David E Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
Multivariate f-divergence Estimation With Confidence Kevin Moon, Alfred Hero
Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis, Adam Klivans
Distributed Balanced Clustering via Mapping Coresets Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni
Automated Variational Inference for Gaussian Process Models Trung V Nguyen, Edwin V. Bonilla
Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu, Justin Domke
Projective dictionary pair learning for pattern classification Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng
Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
Structure learning of antiferromagnetic Ising models Guy Bresler, David Gamarnik, Devavrat Shah
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper
Rounding-based Moves for Metric Labeling M. Pawan Kumar
Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
Efficient learning by implicit exploration in bandit problems with side observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
Tree-structured Gaussian Process Approximations Thang D Bui, Richard E Turner
Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian, Nuno Vasconcelos
Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
Spectral k-Support Norm Regularization Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos
Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Edward Rasmussen
Self-Paced Learning with Diversity Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann
Information-based learning by agents in unbounded state spaces Shariq A Mobin, James A Arnemann, Fritz Sommer
Algorithm selection by rational metareasoning as a model of human strategy selection Falk Lieder, Dillon Plunkett, Jessica B Hamrick, Stuart Russell, Nicholas Hay, Tom Griffiths
The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
General Stochastic Networks for Classification Matthias Zöhrer, Franz Pernkopf
Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup
Orbit Regularization Renato Negrinho, Andre Martins
Analog Memories in a Balanced Rate-Based Network of E-I Neurons Dylan Festa, Guillaume Hennequin, Mate Lengyel
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
Bayesian Inference for Structured Spike and Slab Priors Michael R Andersen, Ole Winther, Lars K. Hansen
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan J Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
Expectation-Maximization for Learning Determinantal Point Processes Jennifer A Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar
Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
Computing Nash Equilibria in Generalized Interdependent Security Games Hau Chan, Luis E. Ortiz
Sensory Integration and Density Estimation Joseph G Makin, Philip N. Sabes
Exploiting easy data in online optimization Amir Sani, Gergely Neu, Alessandro Lazaric
Randomized Experimental Design for Causal Graph Discovery Huining Hu, Zhentao Li, Adrian R Vetta
Extended and Unscented Gaussian Processes Daniel M Steinberg, Edwin V. Bonilla
Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q Ngo, Xuanlong Nguyen, Christopher Ré, Venu Govindaraju
Learning to Optimize via Information-Directed Sampling Daniel Russo, Benjamin Van Roy
Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes, Yonatan Gur, Assaf Zeevi
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network David Eigen, Christian Puhrsch, Rob Fergus
Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G Andersen, Alexander J Smola, Kai Yu
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio, Francis Bach, Simon Lacoste-Julien
A* Sampling Chris J Maddison, Daniel Tarlow, Tom Minka
A Boosting Framework on Grounds of Online Learning Tofigh Naghibi Mohamadpoor, Beat Pfister
Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
Multi-scale Graphical Models for Spatio-Temporal Processes firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
Optimal decision-making with time-varying evidence reliability Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
Recursive Context Propagation Network for Semantic Scene Labeling Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, Inderjit S Dhillon
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators Kai Zhong, Ian En-Hsu Yen, Inderjit S Dhillon, Pradeep K Ravikumar
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit Karin C Knudson, Jacob Yates, Alexander Huk, Jonathan W Pillow
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights Weijie Su, Stephen Boyd, Emmanuel Candes
Graph Clustering With Missing Data: Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
Weakly-supervised Discovery of Visual Pattern Configurations Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
Median Selection Subset Aggregation for Parallel Inference Xiangyu Wang, Peichao Peng, David B Dunson
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour
Clustering from Labels and Time-Varying Graphs Shiau Hong Lim, Yudong Chen, Huan Xu
Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan, Lawrence Carin
Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy Liang
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
Deterministic Symmetric Positive Semidefinite Matrix Completion William E Bishop, Byron M. Yu
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang, Alex Schwing, Raquel Urtasun
Inferring synaptic conductances from spike trains with a biophysically inspired point process model Kenneth W Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W Pillow
Robust Tensor Decomposition with Gross Corruption Quanquan Gu, Huan Gui, Jiawei Han
Structure Regularization for Structured Prediction Xu Sun
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning Robert Lindsey, Mohammad Khajah, Michael Mozer
Optimal rates for k-NN density and mode estimation Sanjoy Dasgupta, Samory Kpotufe
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
Mind the Nuisance: Gaussian Process Classification using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
Global Belief Recursive Neural Networks Romain Paulus, Richard Socher, Christopher D. Manning
Fast and Robust Least Squares Estimation in Corrupted Linear Models Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M Buhmann
Ranking via Robust Binary Classification Hyokun Yun, Parameswaran Raman, S. Vishwanathan
Nonparametric Bayesian inference on multivariate exponential families William R Vega-Brown, Marek Doniec, Nicholas G Roy
Advances in Learning Bayesian Networks of Bounded Treewidth Siqi Nie, Denis D. Maua, Cassio P de Campos, Qiang Ji
Convolutional Neural Network Architectures for Matching Natural Language Sentences Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen
Fast Training of Pose Detectors in the Fourier Domain João F. Henriques, Pedro Martins, Rui F Caseiro, Jorge Batista
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
Subspace Embeddings for the Polynomial Kernel Haim Avron, Huy Nguyen, David Woodruff
Scale Adaptive Blind Deblurring Haichao Zhang, Jianchao Yang
Iterative Neural Autoregressive Distribution Estimator NADE-k Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio
Model-based Reinforcement Learning and the Eluder Dimension Ian Osband, Benjamin Van Roy
Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment Sahar Akram, Jonathan Z Simon, Shihab A Shamma, Behtash Babadi
Probabilistic Differential Dynamic Programming Yunpeng Pan, Evangelos Theodorou
Smoothed Gradients for Stochastic Variational Inference Stephan Mandt, David Blei
Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell, Rachel Ward, Nati Srebro
Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh, Devavrat Shah
Multiscale Fields of Patterns Pedro Felzenszwalb, John G Oberlin
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A Vandermeulen, Clayton Scott
Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, Hartmut Neven
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Mateusz Malinowski, Mario Fritz
Multi-Class Deep Boosting Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
Neural Word Embedding as Implicit Matrix Factorization Omer Levy, Yoav Goldberg
Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang
Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher, Sundeep Rangan
A framework for studying synaptic plasticity with neural spike train data Scott Linderman, Christopher H Stock, Ryan P. Adams
A Representation Theory for Ranking Functions Harsh H Pareek, Pradeep K Ravikumar
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
Self-Adaptable Templates for Feature Coding Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
DFacTo: Distributed Factorization of Tensors Joon Hee Choi, S. Vishwanathan
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm Jun Zhu, Junhua Mao, Alan L. Yuille
Spectral Learning of Mixture of Hidden Markov Models Cem Subakan, Johannes Traa, Paris Smaragdis
Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy Liang, Christopher D. Manning
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
Spectral Methods for Supervised Topic Models Yining Wang, Jun Zhu
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu
Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
Sparse Bayesian structure learning with “dependent relevance determination” priors Anqi Wu, Mijung Park, Oluwasanmi O Koyejo, Jonathan W Pillow
A Filtering Approach to Stochastic Variational Inference Neil Houlsby, David Blei
The limits of squared Euclidean distance regularization Michal Derezinski, Manfred K. Warmuth
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process Bahadir Ozdemir, Larry S. Davis
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F Balcan, Le Song
Positive Curvature and Hamiltonian Monte Carlo Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics Sergey Levine, Pieter Abbeel
Learning with Fredholm Kernels Qichao Que, Mikhail Belkin, Yusu Wang
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) Anshumali Shrivastava, Ping Li
Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar, Ambedkar Dukkipati
Local Decorrelation For Improved Pedestrian Detection Woonhyun Nam, Piotr Dollar, Joon Hee Han
On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I Jordan
Deep Recursive Neural Networks for Compositionality in Language Ozan Irsoy, Claire Cardie
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister, Javier S Turek
A statistical model for tensor PCA Emile Richard, Andrea Montanari
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen
Multi-Scale Spectral Decomposition of Massive Graphs Si Si, Donghyuk Shin, Inderjit S Dhillon, Beresford N Parlett
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang
Sparse Multi-Task Reinforcement Learning Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S Dhillon
Provable Tensor Factorization with Missing Data Prateek Jain, Sewoong Oh
Local Linear Convergence of Forward--Backward under Partial Smoothness Jingwei Liang, Jalal Fadili, Gabriel Peyré
Recursive Inversion Models for Permutations Christopher Meek, Marina Meila
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun
Stochastic Network Design in Bidirected Trees xiaojian wu, Daniel R. Sheldon, Shlomo Zilberstein
(Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen, Tian Lin, Irwin King, Michael R Lyu, Wei Chen
Learning Optimal Commitment to Overcome Insecurity Avrim Blum, Nika Haghtalab, Ariel D Procaccia
Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
Learning Shuffle Ideals Under Restricted Distributions Dongqu Chen
Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F Balcan, Le Song
Spectral Clustering of graphs with the Bethe Hessian Alaa Saade, Florent Krzakala, Lenka Zdeborová
Reputation-based Worker Filtering in Crowdsourcing Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
Predicting Useful Neighborhoods for Lazy Local Learning Aron Yu, Kristen Grauman
Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
General Table Completion using a Bayesian Nonparametric Model Isabel Valera, Zoubin Ghahramani
Minimax-optimal Inference from Partial Rankings Bruce Hajek, Sewoong Oh, Jiaming Xu
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets Jie Wang, Jieping Ye
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R Benson, Jason Lee, Bartek Rajwa, David F Gleich
Diverse Randomized Agents Vote to Win Albert Jiang, Leandro Soriano Marcolino, Ariel D Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon
On the Information Theoretic Limits of Learning Ising Models Rashish Tandon, Karthikeyan Shanmugam, Pradeep K Ravikumar, Alexandros G Dimakis
A Dual Algorithm for Olfactory Computation in the Locust Brain Sina Tootoonian, Mate Lengyel
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
Discovering, Learning and Exploiting Relevance Cem Tekin, Mihaela van der Schaar
Decomposing Parameter Estimation Problems Khaled S Refaat, Arthur Choi, Adnan Darwiche
Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona
Attentional Neural Network: Feature Selection Using Cognitive Feedback Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang
Learning Mixtures of Ranking Models Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar
Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein
Greedy Subspace Clustering Dohyung Park, Constantine Caramanis, Sujay Sanghavi
Reducing the Rank in Relational Factorization Models by Including Observable Patterns Maximilian Nickel, Xueyan Jiang, Volker Tresp
Sparse PCA with Oracle Property Quanquan Gu, Zhaoran Wang, Han Liu
Mode Estimation for High Dimensional Discrete Tree Graphical Models Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao
Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F Balcan, Vandana Kanchanapally, David Woodruff
Efficient Minimax Signal Detection on Graphs Jing Qian, Venkatesh Saligrama
Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin
Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
PAC-Bayesian AUC classification and scoring James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
On Integrated Clustering and Outlier Detection Lionel Ott, Linsey Pang, Fabio T Ramos, Sanjay Chawla
A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H Chen, Devavrat Shah
Active Learning and Best-Response Dynamics Maria-Florina F Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song
Estimation with Norm Regularization Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov
Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan
Distributed Parameter Estimation in Probabilistic Graphical Models Yariv D Mizrahi, Misha Denil, Nando de Freitas
Efficient Optimization for Average Precision SVM Pritish Mohapatra, C.V. Jawahar, M. Pawan Kumar
From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
Analysis of Learning from Positive and Unlabeled Data Marthinus C du Plessis, Gang Niu, Masashi Sugiyama
Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos
Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew G Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
Generalized Unsupervised Manifold Alignment Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
Testing Unfaithful Gaussian Graphical Models De Wen Soh, Sekhar C Tatikonda
Optimistic Planning in Markov Decision Processes Using a Generative Model Balázs Szörényi, Gunnar Kedenburg, Remi Munos
Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, José Bento, Bill Freeman
Generalized Dantzig Selector: Application to the k-support norm Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
Best-Arm Identification in Linear Bandits Marta Soare, Alessandro Lazaric, Remi Munos
Global Sensitivity Analysis for MAP Inference in Graphical Models Jasper De Bock, Cassio P de Campos, Alessandro Antonucci
Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
On the Number of Linear Regions of Deep Neural Networks Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
Message Passing Inference for Large Scale Graphical Models with High Order Potentials Jian Zhang, Alex Schwing, Raquel Urtasun
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
Learning Chordal Markov Networks by Dynamic Programming Kustaa Kangas, Mikko Koivisto, Teppo Niinimäki
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Li F Fei-Fei

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