icml16.md

icml16 accepted papers

1 No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing

Nihar Shah, UC Berkeley; Dengyong Zhou, Microsoft Research

2 Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

Nihar Shah, UC Berkeley; Sivaraman Balakrishnan, CMU; Aditya Guntuboyina, UC Berkeley; Martin Wainwright, UC Berkeley

http://arxiv.org/abs/1510.05610

3 Uprooting and Rerooting Graphical Models

Adrian Weller, University of Cambridge

4 A Deep Learning Approach to Unsupervised Ensemble Learning

Uri Shaham, Yale University; Xiuyuan Cheng, ; Omer Dror, ; Ariel Jaffe, ; Boaz Nadler, ; Joseph Chang, ; Yuval Kluger,

https://arxiv.org/abs/1602.02285

5 Revisiting Semi-Supervised Learning with Graph Embeddings

Zhilin Yang, Carnegie Mellon University; Ruslan Salakhudinov, U. of Toronto; William Cohen, CMU

https://arxiv.org/abs/1603.08861

6 Inverse Optimal Control with Deep Networks via Policy Optimization

Chelsea Finn, UC Berkeley; Sergey Levine, ; Pieter Abbeel, Berkeley

http://arxiv.org/abs/1603.00448

7 Diversity-Promoting Bayesian Learning of Latent Variable Models

Pengtao Xie, Carnegie Mellon University; Jun Zhu, Tsinghua; Eric Xing, CMU

8 Additive Approximations in High Dimensional Regression via the SALSA

Kirthevasan Kandasamy, Carnegie Mellon University; Yaoliang Yu,

paper code

9 Hawkes Processes with Stochastic Excitations

Young Lee, NICTA; Kar Wai Lim, ANU; Cheng Soon Ong, NICTA

10 Data-driven Rank Breaking for Efficient Rank Aggregation

Sewoong Oh, UIUC; Ashish Khetan, UIUC

http://arxiv.org/abs/1601.05495

11 Dropout distillation

Samuel Rota Bulò, FBK; Lorenzo Porzi, FBK; Peter Kontschieder, Microsoft Research Cambridge

12 Metadata-conscious anonymous messaging

Giulia Fanti, UIUC; Peter Kairouz, UIUC; Sewoong Oh, UIUC; Pramod Viswanath, UIUC; kannan ramchandran,

icml2016 nips2015 ieee-extension

13 The Teaching Dimension of Linear Learners

Ji Liu, University of Rochester; Xiaojin Zhu, University of Wisconsin; Hrag Ohannessian, University of Wisconsin-Madison

http://arxiv.org/abs/1512.02181

14 Truthful Univariate Estimators

Ioannis Caragiannis, University of Patras; Ariel Procaccia, Carnegie Mellon University; Nisarg Shah, Carnegie Mellon University

http://www.cs.cmu.edu/~nkshah/papers/Truthful_Univariate_Estimators.pdf

15 Why Regularized Auto-Encoders learn Sparse Representation?

Devansh Arpit, SUNY Buffalo; Yingbo Zhou, SUNY Buffalo; Hung Ngo, SUNY Buffalo; Venu Govindaraju, SUNY Buffalo

http://arxiv.org/abs/1505.05561

16 k-variates++: more pluses in the k-means++

Richard Nock, Nicta & ANU; Raphael Canyasse, Ecole Polytechnique and The Technion; Roksana Boreli, Data61; Frank Nielsen, Ecole Polytechnique and Sony CS Labs Inc.

http://arxiv.org/abs/1602.01198

17 Multi-Player Bandits — a Musical Chairs Approach

Jonathan Rosenski, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of Science; Liran Szlak, Weizmann Institute of Science

http://arxiv.org/abs/1512.02866

18 The Information Sieve

Greg Ver Steeg, Information Sciences Institute; Aram Galstyan, Information Sciences Institute

paper code

19 End-to-End Speech Recognition in English and Mandarin

Sanjeev Satheesh, Baidu SVAIL; Dario Amodei, ; Rishita Anubhai, ; Eric Battenberg, ; Carl Case, ; Jared Casper, ; Bryan Catanzaro, ; Mike Chrzanowski, Baidu USA, Inc.; Adam Coates, ; Greg Diamos, Baidu USA, Inc.; Erich Elsen, Baidu USA, Inc.; Jesse Engel, ; Christopher Fougner, ; Awni Hannun, Baidu USA, Inc.; Billy Jun, ; Patrick LeGresley, ; Sharan Narang, ; Andrew Ng, ; Sherjil Ozair, ; Ryan Prenger, ; Jonathan Raiman, ; David Seetapun, ; Shubho Sengupta, ; Chong Wang, ; Yi Wang, ; Libby Lin, ; Tony Han, ; zhenyao Zhu, ; Dani Yogatama, ; Bo Xiao, ; JingDong Chen, ; Zhiqian Wang, ; Jun Zhan, ; Linxi Fan,

http://arxiv.org/abs/1512.02595

20 On the Consistency of Feature Selection With Lasso for Non-linear Targets

Yue Zhang, Case Western Reserve University; Weihong Guo, Case Western Reserve University; Soumya Ray,

21 Minimum Regret Search for Single- and Multi-Task Optimization

Jan Hendrik Metzen, University Bremen

https://arxiv.org/abs/1602.01064

22 CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy

Nathan Dowlin, Princeton; Ran , ; Kim Laine, Microsoft Research; Kristin Lauter, Microsoft Research; Michael Naehrig, Microsoft Research; John Wernsing, Microsoft Research

microsoft princeton

23 The Variational Nystrom method for large-scale spectral problems

Max Vladymyrov, Yahoo Labs; Miguel Carreira-Perpinan, UC Merced

24 MBA: Multi-Bias Non-linear Activation in Deep Neural Networks

Hongyang Li, The Chinese Univ. of HK; Wanli Ouyang, ; Xiaogang Wang,

https://arxiv.org/abs/1604.00676

25 Asymmetric Multi-task Learning based on Task Relatedness and Confidence

Giwoong Lee, UNIST; Eunho Yang, IBM Research; Sung ju Hwang, UNIST

26 Accurate Robust and Efficient Error Estimation for Decision Trees

Lixin Fan, Nokia Technologies

27 Fast Stochastic Algorithms for SVD and PCA: Convergence Properties and Convexity

Ohad Shamir, Weizmann Institute of Science

http://arxiv.org/abs/1507.08788

28 Convergence of Stochastic Gradient Descent for PCA

Ohad Shamir, Weizmann Institute of Science

http://arxiv.org/abs/1509.09002

29 Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Andrew Lan, Rice University; Tom Goldstein, University of Maryland; Christoph Studer, Cornell University; Richard Baraniuk, Rice University

30 A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation

qiang liu, ; Jason Lee, UC Berkeley; Michael ,

http://arxiv.org/abs/1602.03253

31 Variable Elimination in the Fourier Domain

Yexiang Xue, Cornell University; Stefano Ermon, ; Ronan Le Bras, Cornell University; Carla , ; Bart ,

http://arxiv.org/abs/1508.04032

32 Low-Rank Matrix Approximation with Stability

Dongsheng Li, IBM Research – China; Chao Chen, ; Qin Lv, ; Junchi Yan, ; Li Shang, ; Stephen Chu,

33 Linking losses for density ratio and class-probability estimation

Aditya Menon, National ICT Australia; Cheng Soon Ong, NICTA

34 Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi, Carnegie Mellon University; Ahmed Hefny, ; Suvrit Sra, ; Barnabas Poczos, ; Alex ,

http://arxiv.org/abs/1603.06160

35 Hierarchical Variational Models

Rajesh Ranganath, ; Dustin Tran, Columbia University; Blei David, Columbia

https://arxiv.org/abs/1511.02386

36 Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams

Roy Adams, Univ. of Massachusetts-Amherst; Nazir Saleheen, ; Edison Thomaz, ; Abhinav Parate, ; Santosh Kumar, ; Ben Marlin,

37 Binary embeddings with structured hashed projections

Anna Choromanska, Courant Institute, NYU; Krzysztof Choromanski, Google Research NYC; Mariusz Bojarski, NVIDIA; Tony Jebara, Columbia; Sanjiv Kumar, ; Yann ,

http://arxiv.org/abs/1511.05212

38 A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Columbia University; Matthew Hoffman, Adobe Research; Blei David, Columbia

http://arxiv.org/abs/1602.02666

39 Adaptive Sampling for SGD by Exploiting Side Information

Siddharth Gopal,

40 Learning from Multiway Data: Simple and Efficient Tensor Regression

Rose Yu, University of Southern Cal; Yan Liu,

41 A Distributed Variational Inference Framework for Unifying Parallel Sparse Gaussian Process Regression Models

Trong Nghia Hoang, NUS; Quang Minh Hoang, NUS; Bryan Kian Hsiang Low, NUS

42 Online Stochastic Linear Optimization under One-bit Feedback

Lijun Zhang, Nanjing University; Tianbao Yang, University of Iowa; Rong Jin, Alibaba Group; Yichi Xiao, Nanjing University; Zhi-hua Zhou,

http://arxiv.org/abs/1509.07728

43 Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

Rodolphe Jenatton, ; Jim Huang, Amazon; Cedric Archambeau,

http://arxiv.org/abs/1512.07422

44 Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Adish Singla, ; Sebastian Tschiatschek, ETH Zurich; Andreas Krause,

45 Learning Simple Algorithms from Examples

Wojciech Zaremba, ; Tomas , ; Armand Joulin, ; Rob Fergus, Facebook AI Research

46 Learning Physical Intuition of Block Towers by Example

Adam Lerer, Facebook AI Research; Rob Fergus, Facebook AI Research

47 Structure Learning of Partitioned Markov Networks

Song Liu, The Inst. of Stats. Math.; Taiji Suzuki, ; Masashi Sugiyama, University of Tokyo; Kenji ,

48 Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient

Tianbao Yang, University of Iowa; Lijun Zhang, Nanjing University; Rong Jin, ; Jinfeng Yi, IBM Research

49 Beyond CCA: Moment Matching for Multi-View Models

Anastasia Podosinnikova, INRIA – ENS; Francis Bach, Inria; Simon Lacoste-Julien, INRIA

50 Fast Methods for Estimating the Numerical Rank of Large Matrices

Shashanka Ubaru, University of Minnesota; Yousef Saad, University of Minnesota

51 Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie, University of Washington; Ross Girshick, Facebook; Ali Farhadi, University of Washington

http://arxiv.org/abs/1511.06335

52 Efficient Private Empirical Risk Minimization for High-dimensional Learning

Shiva Kasiviswanathan, Samsung Research America; Hongxia Jin, Samsung Research America

53 Parameter Estimation for Generalized Thurstone Choice Models

Milan Vojnovic, Microsoft; Seyoung Yun, Microsoft

54 Large-Margin Softmax Loss for Convolutional Neural Networks

Weiyang Liu, Peking University; Yandong Wen, South China University of Technology; Zhiding Yu, Carnegie Mellon University; Meng Yang, Shenzhen University

55 A Random Matrix Approach to Recurrent Neural Networks

Romain Couillet, CentraleSupelec; Gilles Wainrib, ENS Ulm, Paris, France; Hafiz Tiomoko Ali, CentraleSupelec, Gif-sur-Yvette, France; Harry Sevi, ENS Lyon, Lyon, Paris

http://couillet.romain.perso.sfr.fr/docs/conf/NN_ICML.pdf

56 Supervised and Semi-Supervised Text Categorization using One-Hot LSTM for Region Embeddings

Rie Johnson, RJ Research Consulting; Tong Zhang,

http://arxiv.org/abs/1602.02373

57 Optimality of Belief Propagation for Crowdsourced Classification

Jungseul Ok, KAIST; Sewoong Oh, UIUC; Jinwoo Shin, KAIST; Yung Yi, KAIST

58 Stability of Controllers for Gaussian Process Forward Models

Julia Vinogradska, Robert Bosch GmBH; Bastian Bischoff, ; Duy Nguyen-Tuong, ; Anne Romer, ; Henner Schmidt, ; Jan Peters,

59 Learning privately from multiparty data

Jihun Hamm, ; Yingjun Cao, UC-San Diego; Mikhail Belkin,

60 Network Morphism Tao Wei, University at Buffalo;

Changhu Wang, Microsoft Research; Yong Rui, Microsoft Research; Chang Wen Chen,

61 A Kronecker-factored approximate Fisher matrix for convolution layers

Roger Grosse, ; James Martens, University of Toronto

https://arxiv.org/abs/1602.01407

62 Experimental Design on a Budget for Sparse Linear Models and Applications

Sathya Narayanan Ravi, UW Madison; Vamsi Ithapu, UW Madison; Vikas Singh, ; Sterling Johnson, UW Madison

63 Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVM

Anton Osokin, ; Jean-Baptiste Alayrac, ENS; Puneet Dokania, INRIA and Ecole Centrale Paris; Simon Lacoste-Julien, INRIA

64 Exact Exponent in Optimal Rates for Crowdsourcing

Chao Gao, Yale University; Yu Lu, Yale University; Dengyong Zhou, Microsoft Research

65 Augmenting Neural Networks with Reconstructive Decoding Pathways for Large-scale Image Classification

Yuting Zhang, University of Michigan; Kibok Lee, University of Michigan; Honglak Lee, University of Michigan

66 Online Low-Rank Subspace Clustering by Explicit Basis Modeling

Jie Shen, Rutgers University; Ping Li, Rutgers; Huan Xu,

http://arxiv.org/abs/1503.08356

67 A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization

Frank Curtis, Lehigh University

68 Stochastic Quasi-Newton Langevin Monte Carlo

Umut Simsekli, Telecom ParisTech; Roland Badeau, ; Taylan Cemgil, ; Gaël Richard,

69 Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, University of Michigan; Lihong Li, Microsoft

70 Fast Rate Analysis of Some Stochastic Optimization Algorithms

Chao Qu, Nus; Huan Xu, ; Chong jin Ong, Nus

71 Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Ke Li, UC Berkeley; Jitendra Malik,

72 Smooth Imitation Learning

Hoang Le, Caltech; Andrew Kang, ; Yisong Yue, Caltech; Peter Carr,

73 Community Recovery in Graphs with Locality

Yuxin Chen, Stanford University; Govinda Kamath, Stanford University; Changho Suh, KAIST; David Tse, Stanford University

74 Variance Reduction for Faster Non-Convex Optimization

Zeyuan Allen-Zhu, Princeton University; Elad Hazan, Princeton University

75 Loss factorization, weakly supervised learning and label noise robustness

Giorgio Patrini, ANU / Data61; Frank Nielsen, Ecole Polytechnique and Sony CS Labs Inc.; Richard Nock, Nicta & ANU; Marcello Carioni, Max Planck Institute

76 Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Shengjie Wang, University of Washington; Abdel-rahman Mohamed, ; Rich Caruana, Microsoft; Jeff Bilmes, U. of Washington; Matthai Plilipose, ; Matthew Richardson, ; Krzysztof Geras, ; Gregor Urban, UC Irvine; Ozlem Aslan,

77 Doubly Decomposing Nonparametric Tensor Regression

Masaaki Imaizumi, University of Tokyo; Kohei Hayashi, NII

78 Hyperparameter optimization with approximate gradient

Fabian Pedregosa, INRIA

79 SDCA without Duality, Regularization, and Individual Convexity

Shai Shalev-Shwartz, Hebrew University of Jerusalem

80 Heteroscedastic Sequences: Beyond Gaussianity

Oren , ; Shie Mannor, Technion

81 A Neural Autoregressive Approach to Collaborative Filtering

Yin Zheng, Hulu LLC.; Bangsheng Tang, Hulu LLC; Wenkui Ding, Hulu LLC.; Hanning Zhou, Hulu

82 On the Quality of the Initial Basin in Overspecified Neural Networks

Itay Safran, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of Science

83 Primal-Dual Rates and Certificates

Celestine Dünner, IBM Research; Simone Forte, Google; Martin Takac, ; Martin Jaggi,

84 Minimizing the Maximal Loss: How and Why

Shai Shalev-Shwartz, Hebrew University of Jerusalem; Yonatan Wexler, Orcam

85 The Sample Complexity of Subspace Clustering with Missing Data

Daniel Pimentel-Alarcon, UW-Madison; Robert Nowak,

86 Online Learning with Feedback Graphs Without the Graphs

Alon Cohen, Technion; Tamir Hazan, ; Tomer Koren,

87 PAC learning of Probabilistic Automaton based on the Method of Moments

Hadrien Glaude, University of Lille; Olivier Pietquin, Univ. Lille, CRIStAL, UMR 9189, SequeL Team, Villeneuve d’Ascq, 59650, FRANCE

88 Estimating Structured Vector Autoregressive Models

Igor Melnyk, University of Minnesota; Arindam Banerjee,

89 Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends

Christopher Tosh, UC San Diego

90 Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms

Mathieu Blondel, NTT; Masakazu Ishihata, NTT Communication Science Labo; Akinori Fujino, NTT; Naonori Ueda,

91 A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain, INRIA; Amaury Habrard, ; François Laviolette, GRAAL, Université Laval; Emilie Morvant,

92 Correlation Clustering and Biclustering with Locally Bounded Errors

Gregory Puleo, UIUC; Olgica Milenkovic, UIUC

http://arxiv.org/abs/1506.08189

93 PAC Lower Bounds and Efficient Algorithms for The Max KK-Armed Bandit Problem

Yahel David, Technion; Nahum Shimkin, Technion

94 A Comparative Analysis and Study of Multiview Convolutional Neural Network Models for Joint Object Categorization and Pose Estimation

Mohamed Elhoseiny, Rutgers University; Tarek El-Gaaly, Rutgers University; Amr Bakry, Rutgers University; Ahmed Elgammal, Rutgers University

95 BASC: Applying Bayesian Optimization to the Search for Global Minima on Potential Energy Surfaces

Shane Carr, Washington University in St. L; Roman Garnett, Wustl; Cynthia Lo, Washington University in St. Louis

96 On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Yossi Arjevani, Weizmann Institute of Science; Ohad Shamir, Weizmann Institute of Science

97 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Xingguo Li, University of Minnesota; Tuo Zhao, ; Raman Arora, Johns Hopkins University; Han , ; Jarvis Haupt,

98 Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation

David Wipf, Microsoft Research, Beijing

99 Fast k-means with accurate bounds

James Newling, Idiap Research Institute; Francois Fleuret, Idiap research institute

100 Boolean Matrix Factorization and Noisy Completion via Message Passing

Siamak Ravanbakhsh, CMU; Barnabas Poczos, ; Russell Greiner, University of Alberta

101 Convolutional Rectifier Networks as Generalized Tensor Decompositions

Nadav Cohen, Hebrew University of Jerusalem; Amnon Shashua, Mobileye

102 Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, UC Berkeley; Ross Boczar, UC Berkeley; Max Simchowitz, UC Berkeley; mahdi Soltanolkotabi, ; Ben Recht, Berkeley

103 Anytime Exploration for Multi-armed Bandits using Confidence Information

Kwang-Sung Jun, UW-Madison; Robert Nowak,

104 Structured Prediction Energy Networks

David Belanger, University of Massachusetts Am; Andrew McCallum,

105 L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Yuchen Zhang, ; Jason Lee, UC Berkeley; Michael ,

106 Compressive Spectral Clustering

Nicolas TREMBLAY, INRIA Rennes; Gilles Puy, INRIA; Remi Gribonval, INRIA; Pierre Vandergheynst, EPFL

https://arxiv.org/abs/1602.02018

107 Low-rank tensor completion: a Riemannian manifold preconditioning approach

Hiroyuki Kasai, The University of Electro-Comm; Bamdev Mishra, Amazon Development Centre India

108 Provable Non-convex Phase Retrieval with Outliers: Median TruncatedWirtinger Flow

Huishuai Zhang, Syracuse University; Yuejie Chi, Ohio State University; Yingbin Liang, Syracuse University

109 Estimating Maximum Expected Value through Gaussian Approximation

Carlo D’Eramo, Politecnico di Milano; Marcello Restelli, Politecnico di Milano; Alessandro Nuara, Politecnico di Milano

110 Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, University of Wisconsin; Christopher Cox, University of Wisconsin; Timothy Rogers, University of Wisconsin; Robert Nowak,

111 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Yarin Gal, University of Cambridge; Zoubin ,

112 Generative Adversarial Text to Image Synthesis

Scott Reed, ; Zeynep Akata, Max Planck Institute for Informatics; Xinchen Yan, University of Michigan; Lajanugen Logeswaran, University of Michigan – Ann Arbor; Honglak Lee, University of Michigan; Bernt Schiele,

113 Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

Sandhya Prabhakaran, Columbia University; Elham Azizi, Columbia University; Ambrose Carr, Columbia University; Dana Pe’er, Columbia University

114 Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives

Zeyuan Allen-Zhu, Princeton University; Yang Yuan, Cornell

115 Sparse Parameter Recovery from Aggregated Data

Avradeep Bhowmik, University of Texas at Austin; Joydeep , ; Oluwasanmi Koyejo, Stanford University & University of Illinois at Urbana Champaign

116 Deep Structured Energy Based Models for Anomaly Detection

Shuangfei Zhai, Binghamton University; Yu Cheng, IBM Research; Zhongfei Zhang, Binghamton University

117 Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen-Zhu, Princeton University; Zheng Qu, The University of Hong Kong; Peter Richtarik, ; Yang Yuan, Cornell

118 Unitary Evolution Recurrent Neural Networks

Martin Arjovsky, University of Buenos Aires; Amar Shah, University of Cambridge; Yoshua Bengio,

119 Markov Latent Feature Models

Aonan Zhang, Columbia University; John Paisley,

120 The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Yingfei Wang, Princeton University; Chu Wang, ; Warren Powell,

https://arxiv.org/abs/1510.02354

121 A Simple and Provable Algorithm for Sparse CCA

Megasthenis Asteris, University of Texas at Austin; Anastasios Kyrillidis, ; Oluwasanmi Koyejo, Stanford University & University of Illinois at Urbana Champaign; Russell Poldrack, Stanford University

122 Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods

Huikang Liu, CUHK; Weijie Wu, CUHK; Anthony Man-Cho So,

123 Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks

Devansh Arpit, SUNY Buffalo; Yingbo Zhou, SUNY Buffalo; Bhargava Kota, SUNY Buffalo; Venu Govindaraju, SUNY Buffalo

124 Learning to Generate with Memory

Chongxuan Li, Tsinghua University; Jun Zhu, Tsinghua; Bo Zhang, Tsinghua University

125 Learning End-to-end Video Classification with Rank-Pooling

Basura Fernando, ANU Canberra Australia; Stephen Gould, Australian National University

126 Learning to Filter with Predictive State Inference Machines

Wen Sun, Carnegie Mellon University; Arun Venkatraman, Carnegie Mellon University; Byron Boots, ; J.Andrew Bagnell, Carnegie Mellon University

127 A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling

Mostafa Rahmani, University of Central Florida; Geroge Atia, University of Central Florida

https://arxiv.org/abs/1502.00182

128 DCM Bandits: Learning to Rank with Multiple Clicks

Sumeet Katariya, University of Wisconsin Madiso; Branislav Kveton, ; Csaba Szepesvari, Alberta; Zheng Wen, Adobe Research

129 Train faster, generalize better: Stability of stochastic gradient descent

Moritz Hardt, Google; Ben Recht, Berkeley; Yoram ,

130 Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm

Junpei Komiyama, The University of Tokyo; Junya Honda, The University of Tokyo; Hiroshi Nakagawa, The University of Tokyo

131 Contextual Combinatorial Cascading Bandits

Shuai Li, CUHK; Baoxiang Wang, ; Shengyu Zhang, ; Wei Chen,

132 Conservative Bandits

Roshan Shariff, University of Alberta; Yifan Wu, ; Tor , ; Csaba Szepesvari, Alberta

133 Variance-Reduced and Projection-Free Stochastic Optimization

Elad Hazan, Princeton University; Haipeng Luo,

134 Factored Temporal Sigmoid Belief Networks for Sequence Learning

Jiaming Song, Tsinghua University; Zhe Gan, Duke University; Lawrence Carin,

135 False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking

QianQian Xu, IIE, CAS; Jiechao Xiong, Peking University; Xiaochun Cao, Institute of information engineering, CAS; Yuan Yao, Peking University

136 Strongly-Typed Recurrent Neural Networks

David Balduzzi, ; Muhammad Ghifary, Victoria University Wellington; Weta Digital

137 Distributed Clustering of Linear Bandits in Peer to Peer Networks

Nathan Korda, University of Oxford; Balazs Szorenyi, ; Shuai Li, University of Insubria

https://arxiv.org/abs/1604.07706

138 Collapsed Variational Inference for Sum-Product Networks

Han Zhao, Carnegie Mellon University; Tameem Adel, University of Amsterdam; Geoff Gordon, CMU; Brandon Amos, Carnegie Mellon University

139 On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search

Piyush Khandelwal, University of Texas at Austin; Elad Liebman, ; Scott , ; Peter Stone, U. of Texas

140 Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, University of California, Berk; Xi Chen, University of California, Berkeley; Rein Houthooft, Ghent University; John Schulman, University of California, Berkeley; Pieter Abbeel, Berkeley

141 KK-Means Clustering with Distributed Dimensions

Hu Ding, State University of New York at Buffalo; Lingxiao Huang, ; Yu Liu, Tsinghua University, IIIS; Jian Li,

related paper: (Cohen et al. STOC 2015)

142 Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Dmity Ulyanov, Skolkovo institute of science ; Vadim Lebedev, ; Andrea , ; Victor Lempitsky, Skolkovo Institute of Sci&Tech

143 Fast Constrained Submodular Maximization: Personalized Data Summarization

Baharan Mirzasoleiman, ETH Zurich; Ashwinkumar Badanidiyuru, Google Research; Amin Karbasi, Yale University

144 On the Statistical Limits of Convex Relaxations

Zhaoran Wang, Princeton University; Quanquan Gu, ; Han ,

145 Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ankit Kumar, MetaMind; Ozan Irsoy, MetaMind; Mohit Iyyer, MetaMind; James Bradbury, MetaMind; Ishaan Gulrajani, MetaMind; Victor Zhong, MetaMind; Romain Paulus, MetaMind; Richard Socher,

146 Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin, ; Aurelien Bellet, INRIA; Joseph Salmon, ; Stéphan Clémençon,

147 Solving Ridge Regression using Sketched Preconditioned SVRG

Alon , ; Francesco Orabona, Yahoo; Shai Shalev-Shwartz, Hebrew University of Jerusalem

148 Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control

Prashanth L.A., University of Maryland ; Cheng Jie, University of Maryland – College Park; Michael Fu, University of Maryland – College Park; Steve Marcus, University of Maryland – College Park; Csaba Szepesvari, Alberta

149 Estimating Accuracy from Unlabeled Data: A Bayesian Approach

Emmanouil Antonios Platanios, Carnegie Mellon University; Avinava Dubey, Carnegie Mellon University; Tom Mitchell, Carnegie Mellon University

150 Non-negative Matrix Factorization under Heavy Noise

Jagdeep Pani, Indian Institute of Science; Ravindran Kannan, Microsoft Reseach India; Chiranjib Bhattacharya, ; Navin Goyal, Microsoft Research India

151 Extreme F-measure Maximization using Sparse Probability Estimates

Kalina Jasinska, ; Krzysztof Dembczynski, ; Robert Busa-Fekete, ; Karlson Pfannschmidt, ; Timo Klerx, ; Eyke Hullermeier,

152 Auxiliary Deep Generative Models

Lars Maaløe, Technical University Denmark; Casper Kaae Sønderby, University of Copenhagen; Søren Kaae Sønderby, University of Copenagen; Ole Winther, Technical University of Denmark

153 Importance Sampling Tree for Large-scale Empirical Expectation

Olivier CANEVET, Idiap Research Institut; Cijo Jose, Idiap Research Institute/ Éc ; Francois Fleuret, Idiap research institute

154 Starting Small – Learning with Adaptive Sample Sizes

Hadi Daneshmand, ETH Zurich; Aurelien Lucchi, ETHZ; Thomas Hofmann,

155 Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang Bui, University of Cambridge; Daniel Hernandez-Lobato, ; Yingzhen Li, University of Cambridge; Jose miguel Hernandez-Lobato, ; Richard Turner, University of Cambridge

156 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Jovana Mitrovic, University of Oxford; Dino Sejdinovic, University of Oxford; Yee-Whye Teh, Oxford

157 Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernandez-Lobato, ; Jose miguel Hernandez-Lobato, ; Amar Shah, University of Cambridge; Ryan Adams, Harvard

158 Rich Component Analysis

Rong Ge, ; James Zou,

159 Black-Box Alpha Divergence Minimization

Jose miguel Hernandez-Lobato, ; Yingzhen Li, University of Cambridge; Mark Rowland, University of Cambridge; Thang Bui, University of Cambridge; Daniel Hernandez-Lobato, ; Richard Turner, University of Cambridge

160 One-Shot Generalization in Deep Generative Models

Danilo Rezende, Google DeepMind; Shakir , ; Daan Wierstra, Google DeepMind

161 Optimal Classification with Multivariate Losses

Nagarajan Natarajan, Microsoft Research India; Oluwasanmi Koyejo, Stanford University & University of Illinois at Urbana Champaign; Pradeep Ravikumar, UT Austin; Inderjit ,

162 A ranking approach to global optimization

Cedric Malherbe, ENS Cachan; Emile Contal, ENS Cachan; Nicolas Vayatis, ENS Cachan

163 Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

Yu-Xiang Wang, ; Veeranjaneyulu Sadhanala, ; Wei Dai, Carnegie Mellon University; Willie Neiswanger, ; Suvrit Sra, ; Eric Xing, CMU

164 Autoencoding beyond pixels using a learned similarity metric

Anders B. L. Larsen, Technical University of Denmar; Søren Kaae Sønderby, University of Copenagen; Hugo Larochelle, Twitter; Ole Winther, Technical University of Denmark

165 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Christopher De Sa, Stanford; Chris Re, Stanford University; Kunle Olukotun, Stanford

166 Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

Atsushi Shibagaki, Nagoya Institute of Technology; Masayuki Karasuyama, Nagoya Institute of Technology; Kohei Hatano, Kyushu University; Ichiro Takeuchi,

167 Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne, Université Paris Diderot; Vianney Perchet,

168 Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design

William Hoiles, UCLA; Mihaela van der Schaar,

169 Mixed membership modelling with hierarchical CRMs

Gaurav Pandey, Indian Institute of Science; Ambedkar Dukkipati, Indian Institute of Science

170 From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

Andre Martins, ; Ramon Astudillo, Unbabel

171 Black-box optimization with a politician

Sebastien Bubeck, Microsoft; Yin Tat Lee, MIT

172 Gaussian process nonparametric tensor estimator and its minimax optimality

Heishiro Kanagawa, Tokyo Institute of Technology; Taiji Suzuki, ; Hayato Kobayashi, Yahoo Japan Corporation; Nobuyuki Shimizu, ; Yukihiro Tagami, Yahoo Japan Corporation

173 No-Regret Algorithms for Heavy-Tailed Linear Bandits

Andres Munoz Medina, ; Scott Yang,

174 Extended and Unscented Kitchen Sinks

Edwin Bonilla, UNSW; Daniel Steinberg, DATA61; Alistair Reid, Data61

175 Matrix Eigendecomposition via Doubly Stochastic Riemannian Optimization

Zhiqiang Xu, Institute of Infocomm Research; Peilin Zhao, I2R, ASTAR

176 Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Cornell University; Thorsten Joachims, Cornell; Adith Swaminathan, Cornell University; Ashudeep Singh, Cornell University; Navin Chandak, Google

177 ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission

Jinsung Yoon, University of California, Los ; Ahmed Alaa, University of California, Los Angeles; Scott Hu, University of California, Los Angeles; Mihaela van der Schaar,

178 An optimal algorithm for the Thresholding Bandit Problem

Andrea LOCATELLI, University of Potsdam; Maurilio Gutzeit, Universität Potsdam; Alexandra Carpentier,

179 Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching

Mu Niu, University of Glasgow; Simon Rogers, University of Glasgow; Maurizio Filippone, EURECOM; Dirk Husmeier, University of Glasgow

180 The Deep Neural Matrix Gaussian Process

Christos Louizos, University of Amsterdam; Max Welling, University of Amsterdam / CIFAR

181 Learning Granger Causality for Hawkes Processes

Hongteng Xu, Georgia Tech; Mehrdad Farajtabar, ; Hongyuan ,

182 Neural Variational Inference for Text Processing

Yishu Miao, University of Oxford; Lei Yu, University of Oxford; Phil Blunsom,

183 Dictionary Learning for Massive Matrix Factorization

Arthur Mensch, Inria; Julien Mairal, ; Bertrand Thirion, Inria; Gael Varoquaux,

184 Pixel Recurrent Neural Networks

Aaron Van den Oord, Google Deepmind; Nal Kalchbrenner, Google Deepmind; Koray Kavukcuoglu, Google Deepmind

185 Sequential decision making under uncertainty: Are most decisions easy?

Ozgur Simsek, ; Simon Algorta, ; Amit Kothiyal,

186 Gaussian quadrature for matrix inverse forms with applications

Chengtao Li, MIT; Suvrit Sra, ; Stefanie Jegelka, MIT

187* Train and Test Tightness of LP Relaxations in Structured Prediction

Ofer Meshi, ; Mehrdad Mahdavi, ; Adrian Weller, University of Cambridge; David Sontag, NYU

https://arxiv.org/abs/1511.01419

188 Stochastic Optimization for Multiview Learning using Partial Least Squares

Raman Arora, Johns Hopkins University; Poorya Mianjy, Johns Hopkins University; Teodor Marinov,

189 Hierarchical Compound Poisson Factorization

Mehmet Basbug, Princeton University; Barbara Engelhardt,

190 Opponent Modeling in Deep Reinforcement Learning

He He, ; Jordan , ; Hal Daume, Maryland

191 No penalty no tears: Least squares in high-dimensional linear models

Xiangyu Wang, Duke University; David , ; Chenlei Leng, University of Warwick

192 SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Zheng Qu, University of Hong Kong; Peter Richtarik, ; Martin Takac, ; Olivier Fercoq, LTCI, CNRS, Télécom ParisTech, Université Paris-Saclay, 75013, Paris, France

193 On Graduated Optimization for Stochastic Non-Convex Problems

Elad Hazan, Princeton University; Kfir Yehuda Levy, Technion; Shai Shalev-Shwartz, Hebrew University of Jerusalem

194 Meta-Learning with Memory-Augmented Neural Networks

Adam Santoro, Google DeepMind; Sergey Bartunov, Higher School of Economics; Matthew Botvinick, ; Daan Wierstra, Google DeepMind; Timothy Lillicrap, Google DeepMind

195 The knockoff filter for FDR control in group-sparse and multitask regression

Ran Dai, The University of Chicago; Rina Barber, The University of Chicago

196 Softened Approximate Policy Iteration for Markov Games

Julien Pérolat, Univ. Lille; Bilal Piot, Univ. Lille; Matthieu Geist, ; Bruno Scherrer, ; Olivier Pietquin, Univ. Lille, CRIStAL, UMR 9189, SequeL Team, Villeneuve d’Ascq, 59650, FRANCE

197 Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert Gower, University of Edinburgh; Donald Goldfarb, Columbia University; Peter Richtarik,

198 Class Probability Estimation via Differential Geometric Regularization

Qinxun Bai, Boston University; Steven Rosenberg, Boston University; Zheng Wu, The Mathwork Inc.; Stan Sclaroff, Boston University

199 Exploiting Cyclic Symmetry in Convolutional Neural Networks

Sander Dieleman, Google DeepMind; Jeffrey De Fauw, Google DeepMind; Koray Kavukcuoglu, Google Deepmind

200 Graying the black box: Understanding DQNs

Tom Zahavy, Technion; Nir Ben-Zrihem, ; Shie Mannor, Technion

201 The Sum-Product Theorem: A Foundation for Learning Tractable Models

Abram Friesen, University of Washington; Pedro ,

202 Pareto Frontier Learning with Expensive Correlated Objectives

Amar Shah, University of Cambridge; Zoubin ,

203 Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Google DeepMind; Adria Puigdomenech Badia, Google DeepMind; Mehdi Mirza, ; Alex Graves, Google DeepMind; Timothy Lillicrap, Google DeepMind; Tim Harley, Google DeepMind; David , ; Koray Kavukcuoglu, Google Deepmind

204 A Simple and Strongly-Local Flow-Based Method for Cut Improvement

Luke Veldt, Purdue Unversity; David Gleich, Purdue University; Michael ,

205 Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Qinliang Su, Duke University; xuejun Liao, ; changyou Chen, ; Lawrence Carin,

206 Barron and Covers’ Theory in Supervised Learning and Its Application to Lasso

Masanori Kawakita, Kyushu University; Jun’ichi Takeuchi, Kyushu University

207 Nonparametric canonical correlation analysis

Tomer Michaeli, Technion; Weiran Wang, ; Karen Livescu, TTI Chicago

208 BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Alexander , ; Karthik Sridharan, Yahoo

209 Associative Long Short-Term Memory

Ivo Danihelka, Google DeepMind; Greg Wayne, Google DeepMind; Benigno Uria, Google DeepMind; Nal Kalchbrenner, Google Deepmind; Alex Graves, Google DeepMind

210 Dueling Network Architectures for Deep Reinforcement Learning

Ziyu Wang, Google Inc.; Nando de Freitas, University of Oxford; Tom Schaul, Google Inc.; Matteo Hessel, Google Deepmind; Hado van Hasselt, Google DeepMind; Marc Lanctot, Google Deepmind

211 Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Tohoku University; Yasuaki Hiraoka, ; Kenji ,

212 Learning Convolutional Neural Networks for Graphs

Mathias Niepert, NEC Laboratories Europe; Mohamed Ahmed, NEC Laboratories Europe; Konstantin Kutzkov, NEC Laboratories Europe

213 Persistent RNNs: Stashing Recurrent Weights On-Chip

Greg Diamos, Baidu USA, Inc.; Shubho Sengupta, Baidu USA, Inc.; Bryan Catanzaro, Baidu USA, Inc.; Mike Chrzanowski, Baidu USA, Inc.; Adam Coates, ; Erich Elsen, Baidu USA, Inc.; Jesse Engel, Baidu USA, Inc.; Awni Hannun, Baidu USA, Inc.; Sanjeev Satheesh, Baidu USA, Inc.

214 Recurrent Orthogonal Networks and Long-Memory Tasks

Mikael Henaff, NYU, Facebook; Arthur , ; Yann ,

215 The Arrow of Time in Multivariate Time Series

Stefan Bauer, ETH Zurich; Bernhard Schölkopf, ; Jonas ,

216 Mixture Proportion Estimation via Kernel Embeddings of Distributions

Harish Ramaswamy, ; Clayton , ; Ambuj ,

217 Fast DPP Sampling for Nystrom with Application to Kernel Methods

Chengtao Li, MIT; Stefanie Jegelka, MIT; Suvrit Sra,

218 Complex Embeddings for Simple Link Prediction

Théo Trouillon, Xerox; Johannes Welbl, ; Guillaume Bouchard, ; Sebastian Riedel, ; Eric Gaussier,

219 Interactive Bayesian Hierarchical Clustering

Sharad Vikram, UCSD; Sanjoy Dasgupta, UCSD

http://arxiv.org/abs/1602.03258

220 A Convolutional Attention Network for Extreme Summarization of Source Code

Miltiadis Allamanis, University of Edinburgh, UK; Hao Peng, Peking University, China; Charles ,

221 How to Fake Multiply by a Gaussian Matrix

Vamsi Potluru, Comcast Cable; Michael Kapralov, ; David Woodruff,

222 Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

Ryan Rogers, University of Pennsylvania; Salil Vadhan, Harvard University; Hyun Lim, UCLA; Marco Gaboardi, University at Buffalo

223 Pliable Rejection Sampling

Akram Erraqabi, Inria Lille Nord Europe; Michal Valko, Inria Lille – Nord Europe; Alexandra Carpentier, ; Odalric Maillard, Inria

224 Differentially Private Policy Evaluation

Borja Balle, Lancaster University; Maziar Gomrokchi, McGill University; Doina Precup, McGill

225 Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip Thomas, CMU; Emma ,

226 Discrete Deep Feature Extraction: A Theory and New Architectures

Thomas Wiatowski, ETH Zurich; Michael Tschannen, ETH Zurich; Aleksandar Stanic, ETH Zurich; Philipp Grohs, University of Vienna; Helmut Boelcskei, ETH Zurich

227 Efficient Algorithms for Adversarial Contextual Learning

Vasilis Syrgkanis, Microsoft Research; Akshay Krishnamurthy, Microsoft Research; Robert Schapire, Microsoft Research

228 Training Deep Neural Networks via Direct Loss Minimization

Yang Song, Tsinghua University; Alexander Schwing, ; Richard , ; Raquel Urtasun, U. of Toronto

229 Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification

Kyuyeon Hwang, Seoul National University; Wonyong Sung, Seoul National University

230 Variational inference for Monte Carlo objectives

Andriy Mnih, ; Danilo Rezende, Google DeepMind

231 Hierarchical Decision Making In Electricity Grid Management

Gal Dalal, Technion; Elad Gilboa, Technion; Shie Mannor, Technion

232 Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

Eric Balkanski, ; Baharan Mirzasoleiman, ETH Zurich; Andreas Krause, ; Yaron Singer,

233 Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Wenling Shang, ; Kihyuk Sohn, NEC Laboratories America; Diogo Almeida, Enlitic; Honglak Lee, University of Michigan

234 Isotonic Hawkes Processes

Yichen Wang, Georgia Tech; Bo Xie, ; Bo Dai, Georgia Tech; Nan Du, ; Le Song, Gatech

235 Cross-graph Learning of Multi-relational Associations

Hanxiao Liu, ; Yiming Yang,

236 Markov-modulated marked Poisson processes for check-in data

Jiangwei Pan, Duke University; Vinayak Rao, Purdue University; Pankaj Agarwal, Duke Univeristy; Alan Gelfand, Duke University

237 Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Tudor Achim, Stanford University; Ashish Sabharwal, Allen Institute for AI; Stefano Ermon,

238 On the Power of Distance-Based Learning

Periklis Papakonstantinou, Rutgers University ; Jia Xu, Chinese Academy of Sciences; Guang Yang, ICT, Beijing

239 A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery

Xin Lin, University of Texas at Austin; Ian En-Hsu Yen, University of Texas at Austin; Jiong Zhang, University of Texas at Austin; Pradeep Ravikumar, UT Austin; Inderjit ,

240 Generalized Direct Change Estimation in Ising Model Structure

Farideh Fazayeli, University of Minnesota; Arindam Banerjee,

241 Robust Principal Component Analysis with Side Information

Kai-Yang Chiang, UT Austin; Cho-Jui Hsieh, UC Davis; Inderjit ,

242 Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation

Huan Gui, University of Illinois at Urba; Jiawei Han, university of illinois at urbana-champaign; Quanquan Gu,

http://arxiv.org/abs/1505.04780

243 Early and Reliable Event Detection Using Proximity Space Representation

Maxime Sangnier, LTCI, CNRS, Télécom ParisTech; Jerome Gauthier, CEA; Alain Rakotomamonjy,

244 Stratified Sampling Meets Machine Learning

Edo Liberty, ; Kevin Lang, Yahoo Labs; Konstantin Shmakov, Yahoo Labs

245 Efficient Multi-Instance Learning for Activity Recognition from Time Series Data Using an Auto-Regressive Hidden Markov Model

Xinze Guan, Oregon State University; Raviv Raich, Oregon State University; Weng-keen ,

246 Generalization Properties and Implicit Regularization for Multiple Passes SGM

Junhong Lin, Istituto Italiano di Tecnologi; Raffaello Camoriano, IIT (Italy) and UNIGE (Italy); Lorenzo Rosasco, Istituto Italiano di Tecnologia, Università degli Studi di Genova and MIT

247 Principal Component Projection Without Principal Component Analysis

Roy Frostig, Stanford University; Cameron Musco, Massachusetts Institute of Technology; Christopher Musco, Mass. Institute of Technology; Aaron Sidford, Microsoft Research, New England

http://arxiv.org/abs/1602.06872

248 Recovery guarantee of weighted low-rank approximation via alternating minimization

Yuanzhi Li, Princeton University; Yingyu Liang, Princeton; Andrej Risteski, Princeton University

http://arxiv.org/abs/1602.02262

249 Deconstructing the Ladder Network Architecture

Mohammad Pezeshki, Universite de Montreal; Linxi Fan, ; Philemon Brakel, ; Aaron Courville, ; Yoshua Bengio, U. of Montreal

250 Generalization and Exploration via Randomized Value Functions

Ian Osband, Stanford; Ben , ; Zheng Wen, Adobe Research

251 Evasion and Hardening of Tree Ensemble Classifiers

Alex Kantchelian, University of California, Berk; J. D. Tygar, UC Berkeley; Anthony Joseph, UC Berkeley

252 Dynamic Memory Networks for Visual and Textual Question Answering

Caiming Xiong, MetaMind; Stephen Merity, MetaMind; Richard Socher,

253 Estimating Cosmological Parameters from the Dark-Matter Distribution

Siamak Ravanbakhsh, CMU; Sebastian Fromenteau, Carnegie Mellon Unitversity; Junier Oliva, CMU; Layne Price, Carnegie Mellon Unitversity; Shirley Ho, Carnegie Mellon Unitversity; Barnabas Poczos, ; Jeff Schneider, Carnegie Mellon Unitversity

254 Learning population-level diffusions with generative RNNs

Tatsunori Hashimoto, MIT; David Gifford, MIT; Tommi Jaakkola, MIT

255 Expressiveness of Rectifier Neural Network

Xingyuan Pan, University of Utah; Vivek Srikumar, University of Utah

256 Discrete Distribution Estimation under Local Privacy

Peter Kairouz, UIUC; Keith Bonawitz, Google; Daniel Ramage,

http://arxiv.org/abs/1602.07387

257 Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families which Allow Positive Dependencies

David Inouye, University of Texas at Austin; Pradeep Ravikumar, UT Austin; Inderjit ,

258 A Box-Constrained Approach for Hard Permutation Problems

Cong Han Lim, Univ of Wisconsin – Madison; Steve Wright, University of Wisconsin-Madison

259 Geometric Mean Metric Learning

Pourya Zadeh, Tehran university; Reshad Hosseini, ; Suvrit Sra,

260 Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

Zhuoran Yang, Princeton University; Zhaoran Wang, Princeton University; Han , ; Yonina Eldar, Technion; Tong Zhang,

261 Conditional Bernoulli Mixtures for Multi-label Classification

Cheng Li, Northeastern University; Bingyu Wang, Northeastern University; Virgil Pavlu, Northeastern University; Javed Aslam, Northeastern University

262 Scalable Discrete Sampling as a Multi-Armed Bandit Problem

Yutian Chen, University of Cambridge; Zoubin ,

263 Recycling Randomness with Structure for Sublinear time Kernel Expansions

Krzysztof Choromanski, Google Research NYC; Vikas Sindhwani, Google Research

264 Bidirectional Helmholtz Machines

Jorg Bornschein, University of Montreal; Samira Shabanian, University of Montreal; Asja Fischer, ; Yoshua Bengio, U. of Montreal

265 Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Jacob Abernethy, U. of Michigan; Elad Hazan, Princeton University

266 Preconditioning Kernel Matrices

Kurt Cutajar, EURECOM; Michael Osborne, ; John Cunningham, Columbia University; Maurizio Filippone, EURECOM

267 Greedy Column Subset Selection: New Bounds and Distributed Algorithms

Jason Altschuler, Princeton University; Aditya Bhaskara, ; Gang Fu, Google Research; Vahab Mirrokni, Google Research; Afshin Rostamizadeh, Google; Morteza Zadimoghaddam , Google Research

268 Dynamic Capacity Networks

Amjad Almahairi, ; Nicolas Ballas, ; Tim Cooijmans, University of Montreal; Yin Zheng, Hulu LLC.; Hugo Larochelle, Twitter; Aaron Courville,

269 Pricing a low-regret seller

Hoda Heidari, ; Mohammad Mahdian, Google; Umar Syed, ; Sergei Vassilvitskii, ; Sadra Yazdanbod, Georgia Institute of Technology

270 Estimation from Indirect Supervision with Linear Moments

Aditi Raghunathan, IIT Madras; Roy Frostig, Stanford University; John , ; Percy Liang, Stanford

271 Speeding up k-means by approximating Euclidean distances via block vectors

Thomas Bottesch, Ulm University; Thomas Bühler, Avira; Markus Kächele, Ulm University

272 Learning and Inference via Maximum Inner Product Search

Stephen Mussmann, Stanford University; Stefano Ermon,

273 A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums

Anton Rodomanov, Higher School of Economics; Dmitry Kropotov, Moscow State University

274 A Kernel Test of Goodness of Fit

Kacper.chwialkowski@ Chwialkowski, Ucl; Heiko Strathmann, University College London; Arthur Gretton, UCL Gatsby

275 Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, University of Oxford; Christian Naesseth, Linköping University; Brooks Paige, University of Oxford; Frank Wood, ; Jan-Willem Vandemeent, ; Fredrik Lindsten, Uppsala University

276 Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Dan Garber, TTI Chicago; Elad Hazan, Princeton University; Chi Jin, UC Berkeley; Sham , ; Cameron Musco, Massachusetts Institute of Technology; Praneeth Netrapalli, Microsoft Research; Aaron Sidford, Microsoft Research, New England

277 A Theory of Generative ConvNet

Jianwen Xie, UCLA; Yang Lu, UCLA; Song-Chun Zhu, UCLA; Yingnian Wu, UCLA

278 Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution

Quanming Yao, HKUST; James Kwok, Hong Kong University Science Technology

279 Computationally Efficient Nystr\”{o}m Approximation using Fast Transforms

Si Si, ; Cho-Jui Hsieh, UC Davis; Inderjit ,

280 Gromov-Wasserstein Barycenters of Similarity Matrices

Gabriel , ; Justin Solomon, ; Marco Cuturi, Kyoto

281 Robust Monte Carlo Sampling using Riemannian Nos'{e}-Poincar'{e} Hamiltonian Dynamics

Anirban Roychowdhury, ; Brian Kulis, Boston University; Srinivasan Parthasarathy,

282 The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ardavan Saeedi, MIT; Matthew Hoffman, Adobe Research; Ryan Adams, Harvard; Matthew Johnson,

283 Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning

Yury Ustinovskiy, Yandex; Valentina Fedorova, Yandex; Gleb Gusev, Yandex; Pavel Serdyukov, Yandex

284 Discriminative Embeddings of Latent Variable Models for Structured Data

Hanjun Dai, Georgia Tech; Bo Dai, Georgia Tech; Le Song, Gatech

285 Robust Random Cut Forest Based Anomaly Detection on Streams

Sudipto Guha, ; Nina Mishra, ; Gourav Roy, ; Okke Schrijvers,

286 Training Neural Networks Without Gradients: A Scalable ADMM Approach

Tom Goldstein, University of Maryland; Gavin Taylor, US Naval Academy; Ankit Patel, Rice University; Ryan Burmeister, US Naval Academy; Zheng Xu, University of Maryland; Bharat Singh, University of Maryland, Colleg

287 Topographical Features of High-Dimensional Categorical Data and Their Applications to Clustering

Chao Chen, CUNY; Novi Quadrianto, University of Sussex

288 Efficient Algorithms for Large-scale Generalized Eigenvector Computation and CCA

Rong Ge, ; Chi Jin, UC Berkeley; Sham , ; Praneeth Netrapalli, Microsoft Research; Aaron Sidford, Microsoft Research, New England

289 Algorithms for Optimizing the Ratio of Submodular Functions

Wenruo Bai, University of Washington; Rishabh Iyer, ; Kai Wei, ; Jeff Bilmes, U. of Washington

290 Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Stanford; Jayesh Gupta, Stanford University; Stefano Ermon,

291 ADIOS: Architectures Deep In Output Space

Moustapha Cisse, ; Maruan Al-Shedivat, CMU; Samy Bengio, Google

292 Causal Strength via Shannon Capacity: Axioms, Estimators and Applications

Weihao Gao, UIUC; Sreeram Kannan, UW Seattle; Sewoong Oh, UIUC; Pramod Viswanath, UIUC

293 Memory-based Control of Active Perception and Action in Minecraft

Junhyuk Oh, University of Michigan; Valliappa Chockalingam, University of Michigan; Satinder , ; Honglak Lee, University of Michigan

294 The Label Complexity of Mixed-Initiative Classifier Training

Jina Suh, Microsoft; Xiaojin Zhu, University of Wisconsin; Saleema Amershi, Microsoft

295 Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Aaron Schein, ; Mingyuan Zhou, ; Blei David, Columbia; Hanna Wallach, Microsoft

296 Tensor Decomposition via Joint Matrix Schur Decomposition

Nikos Vlassis, Adobe; Nicolo Colombo, Univ of Luxembourg

297 Improving the Efficiency of Deep Reinforcement Learning with Normalized Advantage Functions and Synthetic Experience

Shixiang Gu, University of Cambridge; Sergey Levine, Google; Timothy Lillicrap, Google DeepMind; Ilya Sutskever, OpenAI

298 Domain Adaptation with Conditional Transferable Components

Mingming Gong, University of Technology Sydne; Kun Zhang, Carnegie Mellon University; Tongliang Liu, MPI Tuebingen; Dacheng Tao, ; Bernhard Schölkopf,

299 Fixed Point Quantization of Deep Convolutional Networks

Darryl Lin, Qualcomm Research; Sachin Talathi, Qualcomm Research; Sreekanth Annapureddy, NetraDyne Inc.

300 Provable Algorithms for Inference in Topic Models

Sanjeev Arora, Princeton University; Rong Ge, ; Frederic Koehler, Princeton University; Tengyu Ma, Princeton University; Ankur Moitra,

301 Epigraph projections for fast general convex programming

Po-Wei Wang, Carnegie Mellon University; Matt Wytock, ; Zico ,

302 Fast Algorithms for Segmented Regression

Ludwig Schmidt, ; Jerry Li, MIT; Jayadev Acharya, MIT; Ilias Diakonikolas,

303 Energetic Natural Gradient Descent

Philip Thomas, CMU; Christoph Dann, Carnegie Mellon University; Bruno Castro da Silva, ; Emma ,

304 Partition Functions from Rao-Blackwellized Tempered Sampling

Patrick Stinson, Columbia University; David Carlson, Columbia University; Ari Pakman, Columbia University; Liam ,

305 Learning Mixtures of Plackett-Luce Models

Zhibing Zhao, RPI; Peter Piech, RPI; Lirong Xia, RPI

306 Near Optimal Behavior via Approximate State Abstraction

David Abel, Brown University; David Hershkowitz, Brown University; Michael Littman,

307 Power of Ordered Hypothesis Testing

Lihua Lei, Lihua; William Fithian, UC Berkeley, Department of Statistics

308 PHOG: Probabilistic Model for Code

Pavol Bielik, ETH Zurich; Veselin Raychev, ETH Zurich; Martin Vechev, ETH Zurich

309 Shifting Regret, Mirror Descent, and Matrices

Andras Gyorgy, ; Csaba Szepesvari, Alberta

310 Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Jelena Luketina, Aalto University; Tapani Raiko, ; Mathias Berglund, Aalto University

311 Model-Free Trajectory Optimization for Reinforcement Learning of Motor Skills

Riad Akrour, TU Darmstadt; Gerhard Neumann,

312 Controlling the distance to a Kemeny consensus without computing it

Anna Korba, Telecom Paris Tech; Yunlong Jiao, Mines Paris Tech; Eric Sibony, Telecom Paris Tech

313 Horizontally Scalable Submodular Maximization

Mario Lucic, ETH Zurich; Olivier Bachem, ETH Zurich; Morteza Zadimoghaddam , Google Research; Andreas Krause,

314 Group Equivariant Convolutional Networks

Taco Cohen, University of Amsterdam; Max Welling, University of Amsterdam / CIFAR

315 Stochastic Discrete Clenshaw-Curtis Quadrature

Nico Piatkowski, TU Dortmund; Katharina ,

316 Correcting Forecasts with Multi-force Neural Attention

Matthew Riemer, IBM; Aditya Vempaty, IBM; Flavio Calmon, IBM; Fenno Heath, IBM; Richard Hull, IBM; Elham Khabiri, IBM

317 Learning Representations for Counterfactual Inference

Uri Shalit, ; David Sontag, NYU; Fredrik Johansson, Chalmers Technical University

318 The Automatic Statistician: A Relational Perspective

Yunseong Hwang, UNIST; Anh Tong, UNIST; Jaesik Choi, UNIST

319 Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, University of Oxford; Frank Wood,

320 Slice Sampling on Hamiltonian Trajectories

Benjamin Bloem-Reddy, Columbia University; John Cunningham, Columbia University

321 Noisy Activation Functions

Caglar Gülçehre, ; Marcin Moczulski, ; Misha Denil, ; Yoshua Bengio, U. of Montreal

322 A Primal and Dual Sparse Approach to Extreme Classification

Ian En-Hsu Yen, University of Texas at Austin; Xiangru Huang, UTaustin; Pradeep Ravikumar, UT Austin; Kai Zhong, ICES department, University of Texas at Austin; Inderjit ,