Volume 48: International Conference on Machine Learning, 20-22 June 2016, New York, New York, USA

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Editors: Maria Florina Balcan, Kilian Q. Weinberger

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

Nihar Shah, Dengyong Zhou ; PMLR 48:1-10

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

Nihar Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin Wainwright ; PMLR 48:11-20

Uprooting and Rerooting Graphical Models

Adrian Weller ; PMLR 48:21-29

A Deep Learning Approach to Unsupervised Ensemble Learning

Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger ; PMLR 48:30-39

Revisiting Semi-Supervised Learning with Graph Embeddings

Zhilin Yang, William Cohen, Ruslan Salakhudinov ; PMLR 48:40-48

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Chelsea Finn, Sergey Levine, Pieter Abbeel ; PMLR 48:49-58

Diversity-Promoting Bayesian Learning of Latent Variable Models

Pengtao Xie, Jun Zhu, Eric Xing ; PMLR 48:59-68

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

Kirthevasan Kandasamy, Yaoliang Yu ; PMLR 48:69-78

Hawkes Processes with Stochastic Excitations

Young Lee, Kar Wai Lim, Cheng Soon Ong ; PMLR 48:79-88

Data-driven Rank Breaking for Efficient Rank Aggregation

Ashish Khetan, Sewoong Oh ; PMLR 48:89-98

Dropout distillation

Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder ; PMLR 48:99-107

Metadata-conscious anonymous messaging

Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath ; PMLR 48:108-116

The Teaching Dimension of Linear Learners

Ji Liu, Xiaojin Zhu, Hrag Ohannessian ; PMLR 48:117-126

Truthful Univariate Estimators

Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah ; PMLR 48:127-135

Why Regularized Auto-Encoders learn Sparse Representation?

Devansh Arpit, Yingbo Zhou, Hung Ngo, Venu Govindaraju ; PMLR 48:136-144

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

Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen ; PMLR 48:145-154

Multi-Player Bandits – a Musical Chairs Approach

Jonathan Rosenski, Ohad Shamir, Liran Szlak ; PMLR 48:155-163

The Information Sieve

Greg Ver Steeg, Aram Galstyan ; PMLR 48:164-172

Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin

Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, JingDong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich Elsen, Jesse Engel, Linxi Fan, Christopher Fougner, Awni Hannun, Billy Jun, Tony Han, Patrick LeGresley, Xiangang Li, Libby Lin, Sharan Narang, Andrew Ng, Sherjil Ozair, Ryan Prenger, Sheng Qian, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Chong Wang, Yi Wang, Zhiqian Wang, Bo Xiao, Yan Xie, Dani Yogatama, Jun Zhan, Zhenyao Zhu ; PMLR 48:173-182

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

Yue Zhang, Weihong Guo, Soumya Ray ; PMLR 48:183-191

Minimum Regret Search for Single- and Multi-Task Optimization

Jan Hendrik Metzen ; PMLR 48:192-200

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

Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing ; PMLR 48:201-210

The Variational Nystrom method for large-scale spectral problems

Max Vladymyrov, Miguel Carreira-Perpinan ; PMLR 48:211-220

Multi-Bias Non-linear Activation in Deep Neural Networks

Hongyang Li, Wanli Ouyang, Xiaogang Wang ; PMLR 48:221-229

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

Giwoong Lee, Eunho Yang, Sung Hwang ; PMLR 48:230-238

Accurate Robust and Efficient Error Estimation for Decision Trees

Lixin Fan ; PMLR 48:239-247

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

Ohad Shamir ; PMLR 48:248-256

Convergence of Stochastic Gradient Descent for PCA

Ohad Shamir ; PMLR 48:257-265

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer ; PMLR 48:266-275

A Kernelized Stein Discrepancy for Goodness-of-fit Tests

Qiang Liu, Jason Lee, Michael Jordan ; PMLR 48:276-284

Variable Elimination in the Fourier Domain

Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman ; PMLR 48:285-294

Low-Rank Matrix Approximation with Stability

Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu ; PMLR 48:295-303

Linking losses for density ratio and class-probability estimation

Aditya Menon, Cheng Soon Ong ; PMLR 48:304-313

Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola ; PMLR 48:314-323

Hierarchical Variational Models

Rajesh Ranganath, Dustin Tran, David Blei ; PMLR 48:324-333

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

Roy Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin Marlin ; PMLR 48:334-343

Binary embeddings with structured hashed projections

Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun ; PMLR 48:344-353

A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Matthew Hoffman, David Blei ; PMLR 48:354-363

Adaptive Sampling for SGD by Exploiting Side Information

Siddharth Gopal ; PMLR 48:364-372

Learning from Multiway Data: Simple and Efficient Tensor Regression

Rose Yu, Yan Liu ; PMLR 48:373-381

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

Trong Nghia Hoang, Quang Minh Hoang, Bryan Kian Hsiang Low ; PMLR 48:382-391

Online Stochastic Linear Optimization under One-bit Feedback

Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou ; PMLR 48:392-401

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

Rodolphe Jenatton, Jim Huang, Cedric Archambeau ; PMLR 48:402-411

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Adish Singla, Sebastian Tschiatschek, Andreas Krause ; PMLR 48:412-420

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus ; PMLR 48:421-429

Learning Physical Intuition of Block Towers by Example

Adam Lerer, Sam Gross, Rob Fergus ; PMLR 48:430-438

Structure Learning of Partitioned Markov Networks

Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu ; PMLR 48:439-448

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

Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi ; PMLR 48:449-457

Beyond CCA: Moment Matching for Multi-View Models

Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien ; PMLR 48:458-467

Fast methods for estimating the Numerical rank of large matrices

Shashanka Ubaru, Yousef Saad ; PMLR 48:468-477

Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie, Ross Girshick, Ali Farhadi ; PMLR 48:478-487

Efficient Private Empirical Risk Minimization for High-dimensional Learning

Shiva Prasad Kasiviswanathan, Hongxia Jin ; PMLR 48:488-497

Parameter Estimation for Generalized Thurstone Choice Models

Milan Vojnovic, Seyoung Yun ; PMLR 48:498-506

Large-Margin Softmax Loss for Convolutional Neural Networks

Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang ; PMLR 48:507-516

A Random Matrix Approach to Echo-State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi ; PMLR 48:517-525

Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings

Rie Johnson, Tong Zhang ; PMLR 48:526-534

Optimality of Belief Propagation for Crowdsourced Classification

Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi ; PMLR 48:535-544

Stability of Controllers for Gaussian Process Forward Models

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters ; PMLR 48:545-554

Learning privately from multiparty data

Jihun Hamm, Yingjun Cao, Mikhail Belkin ; PMLR 48:555-563

Network Morphism

Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen ; PMLR 48:564-572

A Kronecker-factored approximate Fisher matrix for convolution layers

Roger Grosse, James Martens ; PMLR 48:573-582

Experimental Design on a Budget for Sparse Linear Models and Applications

Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh ; PMLR 48:583-592

Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs

Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien ; PMLR 48:593-602

Exact Exponent in Optimal Rates for Crowdsourcing

Chao Gao, Yu Lu, Dengyong Zhou ; PMLR 48:603-611

Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

Yuting Zhang, Kibok Lee, Honglak Lee ; PMLR 48:612-621

Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit

Jie Shen, Ping Li, Huan Xu ; PMLR 48:622-631

A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization

Frank Curtis ; PMLR 48:632-641

Stochastic Quasi-Newton Langevin Monte Carlo

Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël Richard ; PMLR 48:642-651

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li ; PMLR 48:652-661

Fast Rate Analysis of Some Stochastic Optimization Algorithms

Chao Qu, Huan Xu, Chong Ong ; PMLR 48:662-670

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Ke Li, Jitendra Malik ; PMLR 48:671-679

Smooth Imitation Learning for Online Sequence Prediction

Hoang Le, Andrew Kang, Yisong Yue, Peter Carr ; PMLR 48:680-688

Community Recovery in Graphs with Locality

Yuxin Chen, Govinda Kamath, Changho Suh, David Tse ; PMLR 48:689-698

Variance Reduction for Faster Non-Convex Optimization

Zeyuan Allen-Zhu, Elad Hazan ; PMLR 48:699-707

Loss factorization, weakly supervised learning and label noise robustness

Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni ; PMLR 48:708-717

Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Shengjie Wang, Abdel-rahman Mohamed, Rich Caruana, Jeff Bilmes, Matthai Plilipose, Matthew Richardson, Krzysztof Geras, Gregor Urban, Ozlem Aslan ; PMLR 48:718-726

Doubly Decomposing Nonparametric Tensor Regression

Masaaki Imaizumi, Kohei Hayashi ; PMLR 48:727-736

Hyperparameter optimization with approximate gradient

Fabian Pedregosa ; PMLR 48:737-746

SDCA without Duality, Regularization, and Individual Convexity

Shai Shalev-Shwartz ; PMLR 48:747-754

Heteroscedastic Sequences: Beyond Gaussianity

Oren Anava, Shie Mannor ; PMLR 48:755-763

A Neural Autoregressive Approach to Collaborative Filtering

Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou ; PMLR 48:764-773

On the Quality of the Initial Basin in Overspecified Neural Networks

Itay Safran, Ohad Shamir ; PMLR 48:774-782

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi ; PMLR 48:783-792

Minimizing the Maximal Loss: How and Why

Shai Shalev-Shwartz, Yonatan Wexler ; PMLR 48:793-801

The Information-Theoretic Requirements of Subspace Clustering with Missing Data

Daniel Pimentel-Alarcon, Robert Nowak ; PMLR 48:802-810

Online Learning with Feedback Graphs Without the Graphs

Alon Cohen, Tamir Hazan, Tomer Koren ; PMLR 48:811-819

PAC learning of Probabilistic Automaton based on the Method of Moments

Hadrien Glaude, Olivier Pietquin ; PMLR 48:820-829

Estimating Structured Vector Autoregressive Models

Igor Melnyk, Arindam Banerjee ; PMLR 48:830-839

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

Christopher Tosh ; PMLR 48:840-849

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

Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda ; PMLR 48:850-858

A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant ; PMLR 48:859-868

Correlation Clustering and Biclustering with Locally Bounded Errors

Gregory Puleo, Olgica Milenkovic ; PMLR 48:869-877

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

Yahel David, Nahum Shimkin ; PMLR 48:878-887

A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation

Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed Elgammal ; PMLR 48:888-897

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

Shane Carr, Roman Garnett, Cynthia Lo ; PMLR 48:898-907

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Yossi Arjevani, Ohad Shamir ; PMLR 48:908-916

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt ; PMLR 48:917-925

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

David Wipf ; PMLR 48:926-935

Fast k-means with accurate bounds

James Newling, Francois Fleuret ; PMLR 48:936-944

Boolean Matrix Factorization and Noisy Completion via Message Passing

Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner ; PMLR 48:945-954

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Nadav Cohen, Amnon Shashua ; PMLR 48:955-963

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht ; PMLR 48:964-973

Anytime Exploration for Multi-armed Bandits using Confidence Information

Kwang-Sung Jun, Robert Nowak ; PMLR 48:974-982

Structured Prediction Energy Networks

David Belanger, Andrew McCallum ; PMLR 48:983-992

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Yuchen Zhang, Jason D. Lee, Michael I. Jordan ; PMLR 48:993-1001

Compressive Spectral Clustering

Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst ; PMLR 48:1002-1011

Low-rank tensor completion: a Riemannian manifold preconditioning approach

Hiroyuki Kasai, Bamdev Mishra ; PMLR 48:1012-1021

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

Huishuai Zhang, Yuejie Chi, Yingbin Liang ; PMLR 48:1022-1031

Estimating Maximum Expected Value through Gaussian Approximation

Carlo D’Eramo, Marcello Restelli, Alessandro Nuara ; PMLR 48:1032-1040

Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak ; PMLR 48:1041-1049

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

Yarin Gal, Zoubin Ghahramani ; PMLR 48:1050-1059

Generative Adversarial Text to Image Synthesis

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee ; PMLR 48:1060-1069

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

Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er ; PMLR 48:1070-1079

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

Zeyuan Allen-Zhu, Yang Yuan ; PMLR 48:1080-1089

Sparse Parameter Recovery from Aggregated Data

Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo ; PMLR 48:1090-1099

Deep Structured Energy Based Models for Anomaly Detection

Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang ; PMLR 48:1100-1109

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen-Zhu, Zheng Qu, Peter Richtarik, Yang Yuan ; PMLR 48:1110-1119

Unitary Evolution Recurrent Neural Networks

Martin Arjovsky, Amar Shah, Yoshua Bengio ; PMLR 48:1120-1128

Markov Latent Feature Models

Aonan Zhang, John Paisley ; PMLR 48:1129-1137

The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Yingfei Wang, Chu Wang, Warren Powell ; PMLR 48:1138-1147

A Simple and Provable Algorithm for Sparse Diagonal CCA

Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack ; PMLR 48:1148-1157

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

Huikang Liu, Weijie Wu, Anthony Man-Cho So ; PMLR 48:1158-1167

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

Devansh Arpit, Yingbo Zhou, Bhargava Kota, Venu Govindaraju ; PMLR 48:1168-1176

Learning to Generate with Memory

Chongxuan Li, Jun Zhu, Bo Zhang ; PMLR 48:1177-1186

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

Basura Fernando, Stephen Gould ; PMLR 48:1187-1196

Learning to Filter with Predictive State Inference Machines

Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell ; PMLR 48:1197-1205

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

Mostafa Rahmani, Geroge Atia ; PMLR 48:1206-1214

DCM Bandits: Learning to Rank with Multiple Clicks

Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen ; PMLR 48:1215-1224

Train faster, generalize better: Stability of stochastic gradient descent

Moritz Hardt, Ben Recht, Yoram Singer ; PMLR 48:1225-1234

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

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa ; PMLR 48:1235-1244

Contextual Combinatorial Cascading Bandits

Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen ; PMLR 48:1245-1253

Conservative Bandits

Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari ; PMLR 48:1254-1262

Variance-Reduced and Projection-Free Stochastic Optimization

Elad Hazan, Haipeng Luo ; PMLR 48:1263-1271

Factored Temporal Sigmoid Belief Networks for Sequence Learning

Jiaming Song, Zhe Gan, Lawrence Carin ; PMLR 48:1272-1281

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

QianQian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao ; PMLR 48:1282-1291

Strongly-Typed Recurrent Neural Networks

David Balduzzi, Muhammad Ghifary ; PMLR 48:1292-1300

Distributed Clustering of Linear Bandits in Peer to Peer Networks

Nathan Korda, Balazs Szorenyi, Shuai Li ; PMLR 48:1301-1309

Collapsed Variational Inference for Sum-Product Networks

Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos ; PMLR 48:1310-1318

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

Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone ; PMLR 48:1319-1328

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel ; PMLR 48:1329-1338

K-Means Clustering with Distributed Dimensions

Hu Ding, Yu Liu, Lingxiao Huang, Jian Li ; PMLR 48:1339-1348

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

Dmitry Ulyanov, Vadim Lebedev, Andrea, Victor Lempitsky ; PMLR 48:1349-1357

Fast Constrained Submodular Maximization: Personalized Data Summarization

Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi ; PMLR 48:1358-1367

On the Statistical Limits of Convex Relaxations

Zhaoran Wang, Quanquan Gu, Han Liu ; PMLR 48:1368-1377

Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, Richard Socher ; PMLR 48:1378-1387

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon ; PMLR 48:1388-1396

Solving Ridge Regression using Sketched Preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz ; PMLR 48:1397-1405

Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control

Prashanth L.A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvari ; PMLR 48:1406-1415

Estimating Accuracy from Unlabeled Data: A Bayesian Approach

Emmanouil Antonios Platanios, Avinava Dubey, Tom Mitchell ; PMLR 48:1416-1425

Non-negative Matrix Factorization under Heavy Noise

Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani ; PMLR 48:1426-1434

Extreme F-measure Maximization using Sparse Probability Estimates

Kalina Jasinska, Krzysztof Dembczynski, Robert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hullermeier ; PMLR 48:1435-1444

Auxiliary Deep Generative Models

Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther ; PMLR 48:1445-1453

Importance Sampling Tree for Large-scale Empirical Expectation

Olivier Canevet, Cijo Jose, Francois Fleuret ; PMLR 48:1454-1462

Starting Small - Learning with Adaptive Sample Sizes

Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann ; PMLR 48:1463-1471

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang Bui, Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Yingzhen Li, Richard Turner ; PMLR 48:1472-1481

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

Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh ; PMLR 48:1482-1491

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernandez-Lobato, Jose Hernandez-Lobato, Amar Shah, Ryan Adams ; PMLR 48:1492-1501

Rich Component Analysis

Rong Ge, James Zou ; PMLR 48:1502-1510

Black-Box Alpha Divergence Minimization

Jose Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner ; PMLR 48:1511-1520

One-Shot Generalization in Deep Generative Models

Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra ; PMLR 48:1521-1529

Optimal Classification with Multivariate Losses

Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon ; PMLR 48:1530-1538

A ranking approach to global optimization

Cedric Malherbe, Emile Contal, Nicolas Vayatis ; PMLR 48:1539-1547

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing ; PMLR 48:1548-1557

Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther ; PMLR 48:1558-1566

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Christopher De Sa, Chris Re, Kunle Olukotun ; PMLR 48:1567-1576

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi ; PMLR 48:1577-1586

Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne, Vianney Perchet ; PMLR 48:1587-1595

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

William Hoiles, Mihaela Schaar ; PMLR 48:1596-1604

On collapsed representation of hierarchical Completely Random Measures

Gaurav Pandey, Ambedkar Dukkipati ; PMLR 48:1605-1613

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

Andre Martins, Ramon Astudillo ; PMLR 48:1614-1623

Black-box Optimization with a Politician

Sebastien Bubeck, Yin Tat Lee ; PMLR 48:1624-1631

Gaussian process nonparametric tensor estimator and its minimax optimality

Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami ; PMLR 48:1632-1641

No-Regret Algorithms for Heavy-Tailed Linear Bandits

Andres Munoz Medina, Scott Yang ; PMLR 48:1642-1650

Extended and Unscented Kitchen Sinks

Edwin Bonilla, Daniel Steinberg, Alistair Reid ; PMLR 48:1651-1659

Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization

Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li ; PMLR 48:1660-1669

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims ; PMLR 48:1670-1679

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

Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela Schaar ; PMLR 48:1680-1689

An optimal algorithm for the Thresholding Bandit Problem

Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier ; PMLR 48:1690-1698

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching

Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier ; PMLR 48:1699-1707

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

Christos Louizos, Max Welling ; PMLR 48:1708-1716

Learning Granger Causality for Hawkes Processes

Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha ; PMLR 48:1717-1726

Neural Variational Inference for Text Processing

Yishu Miao, Lei Yu, Phil Blunsom ; PMLR 48:1727-1736

Dictionary Learning for Massive Matrix Factorization

Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux ; PMLR 48:1737-1746

Pixel Recurrent Neural Networks

Aaron Van Oord, Nal Kalchbrenner, Koray Kavukcuoglu ; PMLR 48:1747-1756

Why Most Decisions Are Easy in Tetris—And Perhaps in Other Sequential Decision Problems, As Well

Ozgur Simsek, Simon Algorta, Amit Kothiyal ; PMLR 48:1757-1765

Gaussian quadrature for matrix inverse forms with applications

Chengtao Li, Suvrit Sra, Stefanie Jegelka ; PMLR 48:1766-1775

Train and Test Tightness of LP Relaxations in Structured Prediction

Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag ; PMLR 48:1776-1785

Stochastic Optimization for Multiview Representation Learning using Partial Least Squares

Raman Arora, Poorya Mianjy, Teodor Marinov ; PMLR 48:1786-1794

Hierarchical Compound Poisson Factorization

Mehmet Basbug, Barbara Engelhardt ; PMLR 48:1795-1803

Opponent Modeling in Deep Reinforcement Learning

He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III ; PMLR 48:1804-1813

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

Xiangyu Wang, David Dunson, Chenlei Leng ; PMLR 48:1814-1822

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq ; PMLR 48:1823-1832

On Graduated Optimization for Stochastic Non-Convex Problems

Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz ; PMLR 48:1833-1841

Meta-Learning with Memory-Augmented Neural Networks

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap ; PMLR 48:1842-1850

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

Ran Dai, Rina Barber ; PMLR 48:1851-1859

Softened Approximate Policy Iteration for Markov Games

Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin ; PMLR 48:1860-1868

Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert Gower, Donald Goldfarb, Peter Richtarik ; PMLR 48:1869-1878

Differential Geometric Regularization for Supervised Learning of Classifiers

Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff ; PMLR 48:1879-1888

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu ; PMLR 48:1889-1898

Graying the black box: Understanding DQNs

Tom Zahavy, Nir Ben-Zrihem, Shie Mannor ; PMLR 48:1899-1908

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

Abram Friesen, Pedro Domingos ; PMLR 48:1909-1918

Pareto Frontier Learning with Expensive Correlated Objectives

Amar Shah, Zoubin Ghahramani ; PMLR 48:1919-1927

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu ; PMLR 48:1928-1937

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

Nate Veldt, David Gleich, Michael Mahoney ; PMLR 48:1938-1947

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin ; PMLR 48:1948-1957

Barron and Cover’s Theory in Supervised Learning and its Application to Lasso

Masanori Kawakita, Jun’ichi Takeuchi ; PMLR 48:1958-1966

Nonparametric Canonical Correlation Analysis

Tomer Michaeli, Weiran Wang, Karen Livescu ; PMLR 48:1967-1976

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Alexander Rakhlin, Karthik Sridharan ; PMLR 48:1977-1985

Associative Long Short-Term Memory

Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves ; PMLR 48:1986-1994

Dueling Network Architectures for Deep Reinforcement Learning

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas ; PMLR 48:1995-2003

Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu ; PMLR 48:2004-2013

Learning Convolutional Neural Networks for Graphs

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov ; PMLR 48:2014-2023

Persistent RNNs: Stashing Recurrent Weights On-Chip

Greg Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh ; PMLR 48:2024-2033

Recurrent Orthogonal Networks and Long-Memory Tasks

Mikael Henaff, Arthur Szlam, Yann LeCun ; PMLR 48:2034-2042

The Arrow of Time in Multivariate Time Series

Stefan Bauer, Bernhard Schölkopf, Jonas Peters ; PMLR 48:2043-2051

Mixture Proportion Estimation via Kernel Embeddings of Distributions

Harish Ramaswamy, Clayton Scott, Ambuj Tewari ; PMLR 48:2052-2060

Fast DPP Sampling for Nystrom with Application to Kernel Methods

Chengtao Li, Stefanie Jegelka, Suvrit Sra ; PMLR 48:2061-2070

Complex Embeddings for Simple Link Prediction

Théo Trouillon, Johannes Welbl, Sebastian Riedel, Eric Gaussier, Guillaume Bouchard ; PMLR 48:2071-2080

Interactive Bayesian Hierarchical Clustering

Sharad Vikram, Sanjoy Dasgupta ; PMLR 48:2081-2090

A Convolutional Attention Network for Extreme Summarization of Source Code

Miltiadis Allamanis, Hao Peng, Charles Sutton ; PMLR 48:2091-2100

How to Fake Multiply by a Gaussian Matrix

Michael Kapralov, Vamsi Potluru, David Woodruff ; PMLR 48:2101-2110

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

Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan ; PMLR 48:2111-2120

Pliable Rejection Sampling

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard ; PMLR 48:2121-2129

Differentially Private Policy Evaluation

Borja Balle, Maziar Gomrokchi, Doina Precup ; PMLR 48:2130-2138

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip Thomas, Emma Brunskill ; PMLR 48:2139-2148

Discrete Deep Feature Extraction: A Theory and New Architectures

Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei ; PMLR 48:2149-2158

Efficient Algorithms for Adversarial Contextual Learning

Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire ; PMLR 48:2159-2168

Training Deep Neural Networks via Direct Loss Minimization

Yang Song, Alexander Schwing, Richard, Raquel Urtasun ; PMLR 48:2169-2177

Sequence to Sequence Training of CTC-RNNs with Partial Windowing

Kyuyeon Hwang, Wonyong Sung ; PMLR 48:2178-2187

Variational Inference for Monte Carlo Objectives

Andriy Mnih, Danilo Rezende ; PMLR 48:2188-2196

Hierarchical Decision Making In Electricity Grid Management

Gal Dalal, Elad Gilboa, Shie Mannor ; PMLR 48:2197-2206

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer ; PMLR 48:2207-2216

Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee ; PMLR 48:2217-2225

Isotonic Hawkes Processes

Yichen Wang, Bo Xie, Nan Du, Le Song ; PMLR 48:2226-2234

Cross-Graph Learning of Multi-Relational Associations

Hanxiao Liu, Yiming Yang ; PMLR 48:2235-2243

Markov-modulated Marked Poisson Processes for Check-in Data

Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand ; PMLR 48:2244-2253

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Tudor Achim, Ashish Sabharwal, Stefano Ermon ; PMLR 48:2254-2262

On the Power and Limits of Distance-Based Learning

Periklis Papakonstantinou, Jia Xu, Guang Yang ; PMLR 48:2263-2271

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

Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit Dhillon ; PMLR 48:2272-2280

Generalized Direct Change Estimation in Ising Model Structure

Farideh Fazayeli, Arindam Banerjee ; PMLR 48:2281-2290

Robust Principal Component Analysis with Side Information

Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon ; PMLR 48:2291-2299

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

Huan Gui, Jiawei Han, Quanquan Gu ; PMLR 48:2300-2309

Early and Reliable Event Detection Using Proximity Space Representation

Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy ; PMLR 48:2310-2319

Stratified Sampling Meets Machine Learning

Edo Liberty, Kevin Lang, Konstantin Shmakov ; PMLR 48:2320-2329

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

Xinze Guan, Raviv Raich, Weng-Keen Wong ; PMLR 48:2330-2339

Generalization Properties and Implicit Regularization for Multiple Passes SGM

Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco ; PMLR 48:2340-2348

Principal Component Projection Without Principal Component Analysis

Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford ; PMLR 48:2349-2357

Recovery guarantee of weighted low-rank approximation via alternating minimization

Yuanzhi Li, Yingyu Liang, Andrej Risteski ; PMLR 48:2358-2367

Deconstructing the Ladder Network Architecture

Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio ; PMLR 48:2368-2376

Generalization and Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Zheng Wen ; PMLR 48:2377-2386

Evasion and Hardening of Tree Ensemble Classifiers

Alex Kantchelian, J. D. Tygar, Anthony Joseph ; PMLR 48:2387-2396

Dynamic Memory Networks for Visual and Textual Question Answering

Caiming Xiong, Stephen Merity, Richard Socher ; PMLR 48:2397-2406

Estimating Cosmological Parameters from the Dark Matter Distribution

Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos ; PMLR 48:2407-2416

Learning Population-Level Diffusions with Generative RNNs

Tatsunori Hashimoto, David Gifford, Tommi Jaakkola ; PMLR 48:2417-2426

Expressiveness of Rectifier Networks

Xingyuan Pan, Vivek Srikumar ; PMLR 48:2427-2435

Discrete Distribution Estimation under Local Privacy

Peter Kairouz, Keith Bonawitz, Daniel Ramage ; PMLR 48:2436-2444

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies

David Inouye, Pradeep Ravikumar, Inderjit Dhillon ; PMLR 48:2445-2453

A Box-Constrained Approach for Hard Permutation Problems

Cong Han Lim, Steve Wright ; PMLR 48:2454-2463

Geometric Mean Metric Learning

Pourya Zadeh, Reshad Hosseini, Suvrit Sra ; PMLR 48:2464-2471

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang ; PMLR 48:2472-2481

Conditional Bernoulli Mixtures for Multi-label Classification

Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam ; PMLR 48:2482-2491

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

Yutian Chen, Zoubin Ghahramani ; PMLR 48:2492-2501

Recycling Randomness with Structure for Sublinear time Kernel Expansions

Krzysztof Choromanski, Vikas Sindhwani ; PMLR 48:2502-2510

Bidirectional Helmholtz Machines

Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio ; PMLR 48:2511-2519

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Jacob Abernethy, Elad Hazan ; PMLR 48:2520-2528

Preconditioning Kernel Matrices

Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone ; PMLR 48:2529-2538

Greedy Column Subset Selection: New Bounds and Distributed Algorithms

Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam ; PMLR 48:2539-2548

Dynamic Capacity Networks

Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville ; PMLR 48:2549-2558

Pricing a Low-regret Seller

Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod ; PMLR 48:2559-2567

Estimation from Indirect Supervision with Linear Moments

Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang ; PMLR 48:2568-2577

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

Thomas Bottesch, Thomas Bühler, Markus Kächele ; PMLR 48:2578-2586

Learning and Inference via Maximum Inner Product Search

Stephen Mussmann, Stefano Ermon ; PMLR 48:2587-2596

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

Anton Rodomanov, Dmitry Kropotov ; PMLR 48:2597-2605

A Kernel Test of Goodness of Fit

Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton ; PMLR 48:2606-2615

Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood ; PMLR 48:2616-2625

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford ; PMLR 48:2626-2634

A Theory of Generative ConvNet

Jianwen Xie, Yang Lu, Song-Chun Zhu, Yingnian Wu ; PMLR 48:2635-2644

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Quanming Yao, James Kwok ; PMLR 48:2645-2654

Computationally Efficient Nyström Approximation using Fast Transforms

Si Si, Cho-Jui Hsieh, Inderjit Dhillon ; PMLR 48:2655-2663

Gromov-Wasserstein Averaging of Kernel and Distance Matrices

Gabriel Peyré, Marco Cuturi, Justin Solomon ; PMLR 48:2664-2672

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics

Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy ; PMLR 48:2673-2681

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams ; PMLR 48:2682-2691

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

Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov ; PMLR 48:2692-2701

Discriminative Embeddings of Latent Variable Models for Structured Data

Hanjun Dai, Bo Dai, Le Song ; PMLR 48:2702-2711

Robust Random Cut Forest Based Anomaly Detection on Streams

Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers ; PMLR 48:2712-2721

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein ; PMLR 48:2722-2731

Clustering High Dimensional Categorical Data via Topographical Features

Chao Chen, Novi Quadrianto ; PMLR 48:2732-2740

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford ; PMLR 48:2741-2750

Algorithms for Optimizing the Ratio of Submodular Functions

Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes ; PMLR 48:2751-2759

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh Gupta, Stefano Ermon ; PMLR 48:2760-2769

ADIOS: Architectures Deep In Output Space

Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio ; PMLR 48:2770-2779

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications

Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath ; PMLR 48:2780-2789

Control of Memory, Active Perception, and Action in Minecraft

Junhyuk Oh, Valliappa Chockalingam, Satinder, Honglak Lee ; PMLR 48:2790-2799

The Label Complexity of Mixed-Initiative Classifier Training

Jina Suh, Xiaojin Zhu, Saleema Amershi ; PMLR 48:2800-2809

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach ; PMLR 48:2810-2819

Tensor Decomposition via Joint Matrix Schur Decomposition

Nicolo Colombo, Nikos Vlassis ; PMLR 48:2820-2828

Continuous Deep Q-Learning with Model-based Acceleration

Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine ; PMLR 48:2829-2838

Domain Adaptation with Conditional Transferable Components

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf ; PMLR 48:2839-2848

Fixed Point Quantization of Deep Convolutional Networks

Darryl Lin, Sachin Talathi, Sreekanth Annapureddy ; PMLR 48:2849-2858

Provable Algorithms for Inference in Topic Models

Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra ; PMLR 48:2859-2867

Epigraph projections for fast general convex programming

Po-Wei Wang, Matt Wytock, Zico Kolter ; PMLR 48:2868-2877

Fast Algorithms for Segmented Regression

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt ; PMLR 48:2878-2886

Energetic Natural Gradient Descent

Philip Thomas, Bruno Castro Silva, Christoph Dann, Emma Brunskill ; PMLR 48:2887-2895

Partition Functions from Rao-Blackwellized Tempered Sampling

David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski ; PMLR 48:2896-2905

Learning Mixtures of Plackett-Luce Models

Zhibing Zhao, Peter Piech, Lirong Xia ; PMLR 48:2906-2914

Near Optimal Behavior via Approximate State Abstraction

David Abel, David Hershkowitz, Michael Littman ; PMLR 48:2915-2923

Power of Ordered Hypothesis Testing

Lihua Lei, William Fithian ; PMLR 48:2924-2932

PHOG: Probabilistic Model for Code

Pavol Bielik, Veselin Raychev, Martin Vechev ; PMLR 48:2933-2942

Shifting Regret, Mirror Descent, and Matrices

Andras Gyorgy, Csaba Szepesvari ; PMLR 48:2943-2951

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Jelena Luketina, Tapani Raiko, Mathias Berglund, Klaus Greff ; PMLR 48:2952-2960

Model-Free Trajectory Optimization for Reinforcement Learning

Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki ; PMLR 48:2961-2970

Controlling the distance to a Kemeny consensus without computing it

Yunlong Jiao, Anna Korba, Eric Sibony ; PMLR 48:2971-2980

Horizontally Scalable Submodular Maximization

Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause ; PMLR 48:2981-2989

Group Equivariant Convolutional Networks

Taco Cohen, Max Welling ; PMLR 48:2990-2999

Stochastic Discrete Clenshaw-Curtis Quadrature

Nico Piatkowski, Katharina Morik ; PMLR 48:3000-3009

Correcting Forecasts with Multifactor Neural Attention

Matthew Riemer, Aditya Vempaty, Flavio Calmon, Fenno Heath, Richard Hull, Elham Khabiri ; PMLR 48:3010-3019

Learning Representations for Counterfactual Inference

Fredrik Johansson, Uri Shalit, David Sontag ; PMLR 48:3020-3029

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Yunseong Hwang, Anh Tong, Jaesik Choi ; PMLR 48:3030-3039

Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, Frank Wood ; PMLR 48:3040-3049

Slice Sampling on Hamiltonian Trajectories

Benjamin Bloem-Reddy, John Cunningham ; PMLR 48:3050-3058

Noisy Activation Functions

Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio ; PMLR 48:3059-3068

PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification

Ian En-Hsu Yen, Xiangru Huang, Pradeep Ravikumar, Kai Zhong, Inderjit Dhillon ; PMLR 48:3069-3077

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