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Proceedings of The ICML 2016

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30 March 2017


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Proceedings of The 33rd International Conference on Machine Learning

Editors: Maria Florina Balcan, Kilian Q. Weinberger

Accepted Papers

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

Nihar Shah, Dengyong Zhou

[abs] [pdf] [supplementary]

Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

Nihar Shah, Sivaraman Balakrishnan, Aditya Guntuboyina, Martin Wainwright

[abs] [pdf] [supplementary]

Uprooting and Rerooting Graphical Models

Adrian Weller

[abs] [pdf] [supplementary]

A Deep Learning Approach to Unsupervised Ensemble Learning

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

[abs] [pdf] [supplementary]

Revisiting Semi-Supervised Learning with Graph Embeddings

Zhilin Yang, William Cohen, Ruslan Salakhudinov

[abs] [pdf]

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

Chelsea Finn, Sergey Levine, Pieter Abbeel

[abs] [pdf] [supplementary]

Diversity-Promoting Bayesian Learning of Latent Variable Models

Pengtao Xie, Jun Zhu, Eric Xing

[abs] [pdf] [supplementary]

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

Kirthevasan Kandasamy, Yaoliang Yu

[abs] [pdf] [supplementary]

Hawkes Processes with Stochastic Excitations

Young Lee, Kar Wai Lim, Cheng Soon Ong

[abs] [pdf] [supplementary]

Data-driven Rank Breaking for Efficient Rank Aggregation

Ashish Khetan, Sewoong Oh

[abs] [pdf] [supplementary]

Dropout distillation

Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder

[abs] [pdf] [supplementary]

Metadata-conscious anonymous messaging

Giulia Fanti, Peter Kairouz, Sewoong Oh, Kannan Ramchandran, Pramod Viswanath

[abs] [pdf] [supplementary]

The Teaching Dimension of Linear Learners

Ji Liu, Xiaojin Zhu, Hrag Ohannessian

[abs] [pdf] [supplementary]

Truthful Univariate Estimators

Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah

[abs] [pdf] [supplementary]

Why Regularized Auto-Encoders learn Sparse Representation?

Devansh Arpit, Yingbo Zhou, Hung Ngo, Venu Govindaraju

[abs] [pdf] [supplementary]

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

Richard Nock, Raphael Canyasse, Roksana Boreli, Frank Nielsen

[abs] [pdf] [supplementary]

Multi-Player Bandits – a Musical Chairs Approach

Jonathan Rosenski, Ohad Shamir, Liran Szlak

[abs] [pdf] [supplementary]

The Information Sieve

Greg Ver Steeg, Aram Galstyan

[abs] [pdf] [supplementary]

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

[abs] [pdf]

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

Yue Zhang, Weihong Guo, Soumya Ray

[abs] [pdf]

Minimum Regret Search for Single- and Multi-Task Optimization

Jan Hendrik Metzen

[abs] [pdf] [supplementary]

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

[abs] [pdf]

The Variational Nystrom method for large-scale spectral problems

Max Vladymyrov, Miguel Carreira-Perpinan

[abs] [pdf] [supplementary]

Multi-Bias Non-linear Activation in Deep Neural Networks

Hongyang Li, Wanli Ouyang, Xiaogang Wang

[abs] [pdf]

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

Giwoong Lee, Eunho Yang, Sung ju Hwang

[abs] [pdf]

Accurate Robust and Efficient Error Estimation for Decision Trees

Lixin Fan

[abs] [pdf]

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

Ohad Shamir

[abs] [pdf] [supplementary]

Convergence of Stochastic Gradient Descent for PCA

Ohad Shamir

[abs] [pdf] [supplementary]

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer

[abs] [pdf]

A Kernelized Stein Discrepancy for Goodness-of-fit Tests

Qiang Liu, Jason Lee, Michael Jordan

[abs] [pdf] [supplementary]

Variable Elimination in the Fourier Domain

Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman

[abs] [pdf] [supplementary]

Low-Rank Matrix Approximation with Stability

Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu

[abs] [pdf]

Linking losses for density ratio and class-probability estimation

Aditya Menon, Cheng Soon Ong

[abs] [pdf] [supplementary]

Stochastic Variance Reduction for Nonconvex Optimization

Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alex Smola

[abs] [pdf] [supplementary]

Hierarchical Variational Models

Rajesh Ranganath, Dustin Tran, David Blei

[abs] [pdf] [supplementary]

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

[abs] [pdf]

Binary embeddings with structured hashed projections

Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun

[abs] [pdf] [supplementary]

A Variational Analysis of Stochastic Gradient Algorithms

Stephan Mandt, Matthew Hoffman, David Blei

[abs] [pdf] [supplementary]

Adaptive Sampling for SGD by Exploiting Side Information

Siddharth Gopal

[abs] [pdf]

Learning from Multiway Data: Simple and Efficient Tensor Regression

Rose Yu, Yan Liu

[abs] [pdf]

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

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

[abs] [pdf] [supplementary]

Online Stochastic Linear Optimization under One-bit Feedback

Lijun Zhang, Tianbao Yang, Rong Jin, Yichi Xiao, Zhi-hua Zhou

[abs] [pdf] [supplementary]

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints

Rodolphe Jenatton, Jim Huang, Cedric Archambeau

[abs] [pdf]

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

Adish Singla, Sebastian Tschiatschek, Andreas Krause

[abs] [pdf]

Learning Simple Algorithms from Examples

Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

[abs] [pdf]

Learning Physical Intuition of Block Towers by Example

Adam Lerer, Sam Gross, Rob Fergus

[abs] [pdf]

Structure Learning of Partitioned Markov Networks

Song Liu, Taiji Suzuki, Masashi Sugiyama, Kenji Fukumizu

[abs] [pdf] [supplementary]

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

Tianbao Yang, Lijun Zhang, Rong Jin, Jinfeng Yi

[abs] [pdf] [supplementary]

Beyond CCA: Moment Matching for Multi-View Models

Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien

[abs] [pdf] [supplementary]

Fast methods for estimating the Numerical rank of large matrices

Shashanka Ubaru, Yousef Saad

[abs] [pdf] [supplementary]

Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie, Ross Girshick, Ali Farhadi

[abs] [pdf]

Efficient Private Empirical Risk Minimization for High-dimensional Learning

Shiva Prasad Kasiviswanathan, Hongxia Jin

[abs] [pdf] [supplementary]

Parameter Estimation for Generalized Thurstone Choice Models

Milan Vojnovic, Seyoung Yun

[abs] [pdf] [supplementary]

Large-Margin Softmax Loss for Convolutional Neural Networks

Weiyang Liu, Yandong Wen, Zhiding Yu, Meng Yang

[abs] [pdf]

A Random Matrix Approach to Echo-State Neural Networks

Romain Couillet, Gilles Wainrib, Hafiz Tiomoko Ali, Harry Sevi

[abs] [pdf]

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

Rie Johnson, Tong Zhang

[abs] [pdf]

Optimality of Belief Propagation for Crowdsourced Classification

Jungseul Ok, Sewoong Oh, Jinwoo Shin, Yung Yi

[abs] [pdf] [supplementary]

Stability of Controllers for Gaussian Process Forward Models

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Anne Romer, Henner Schmidt, Jan Peters

[abs] [pdf]

Learning privately from multiparty data

Jihun Hamm, Yingjun Cao, Mikhail Belkin

[abs] [pdf] [supplementary]

Network Morphism

Tao Wei, Changhu Wang, Yong Rui, Chang Wen Chen

[abs] [pdf]

A Kronecker-factored approximate Fisher matrix for convolution layers

Roger Grosse, James Martens

[abs] [pdf] [supplementary]

Experimental Design on a Budget for Sparse Linear Models and Applications

Sathya Narayanan Ravi, Vamsi Ithapu, Sterling Johnson, Vikas Singh

[abs] [pdf]

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

Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien

[abs] [pdf] [supplementary]

Exact Exponent in Optimal Rates for Crowdsourcing

Chao Gao, Yu Lu, Dengyong Zhou

[abs] [pdf] [supplementary]

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

Yuting Zhang, Kibok Lee, Honglak Lee

[abs] [pdf] [supplementary]

Online Low-Rank Subspace Clustering by Basis Dictionary Pursuit

Jie Shen, Ping Li, Huan Xu

[abs] [pdf] [supplementary]

A Self-Correcting Variable-Metric Algorithm for Stochastic Optimization

Frank Curtis

[abs] [pdf] [supplementary]

Stochastic Quasi-Newton Langevin Monte Carlo

Umut Simsekli, Roland Badeau, Taylan Cemgil, Gaël Richard

[abs] [pdf] [supplementary]

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li

[abs] [pdf] [supplementary]

Fast Rate Analysis of Some Stochastic Optimization Algorithms

Chao Qu, Huan Xu, Chong jin Ong

[abs] [pdf] [supplementary]

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Ke Li, Jitendra Malik

[abs] [pdf] [supplementary]

Smooth Imitation Learning for Online Sequence Prediction

Hoang Le, Andrew Kang, Yisong Yue, Peter Carr

[abs] [pdf] [supplementary]

Community Recovery in Graphs with Locality

Yuxin Chen, Govinda Kamath, Changho Suh, David Tse

[abs] [pdf]

Variance Reduction for Faster Non-Convex Optimization

Zeyuan Allen-Zhu, Elad Hazan

[abs] [pdf]

Loss factorization, weakly supervised learning and label noise robustness

Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni

[abs] [pdf] [supplementary]

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

[abs] [pdf]

Doubly Decomposing Nonparametric Tensor Regression

Masaaki Imaizumi, Kohei Hayashi

[abs] [pdf] [supplementary]

Hyperparameter optimization with approximate gradient

Fabian Pedregosa

[abs] [pdf] [supplementary]

SDCA without Duality, Regularization, and Individual Convexity

Shai Shalev-Shwartz

[abs] [pdf]

Heteroscedastic Sequences: Beyond Gaussianity

Oren Anava, Shie Mannor

[abs] [pdf] [supplementary]

A Neural Autoregressive Approach to Collaborative Filtering

Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou

[abs] [pdf] [supplementary]

On the Quality of the Initial Basin in Overspecified Neural Networks

Itay Safran, Ohad Shamir

[abs] [pdf] [supplementary]

Primal-Dual Rates and Certificates

Celestine Dünner, Simone Forte, Martin Takac, Martin Jaggi

[abs] [pdf] [supplementary]

Minimizing the Maximal Loss: How and Why

Shai Shalev-Shwartz, Yonatan Wexler

[abs] [pdf] [supplementary]

The Information-Theoretic Requirements of Subspace Clustering with Missing Data

Daniel Pimentel-Alarcon, Robert Nowak

[abs] [pdf]

Online Learning with Feedback Graphs Without the Graphs

Alon Cohen, Tamir Hazan, Tomer Koren

[abs] [pdf]

PAC learning of Probabilistic Automaton based on the Method of Moments

Hadrien Glaude, Olivier Pietquin

[abs] [pdf] [supplementary]

Estimating Structured Vector Autoregressive Models

Igor Melnyk, Arindam Banerjee

[abs] [pdf] [supplementary]

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

Christopher Tosh

[abs] [pdf] [supplementary]

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

Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda

[abs] [pdf] [supplementary]

A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

[abs] [pdf] [supplementary]

Correlation Clustering and Biclustering with Locally Bounded Errors

Gregory Puleo, Olgica Milenkovic

[abs] [pdf]

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

Yahel David, Nahum Shimkin

[abs] [pdf] [supplementary]

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

[abs] [pdf] [supplementary]

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

Shane Carr, Roman Garnett, Cynthia Lo

[abs] [pdf]

On the Iteration Complexity of Oblivious First-Order Optimization Algorithms

Yossi Arjevani, Ohad Shamir

[abs] [pdf] [supplementary]

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis Haupt

[abs] [pdf] [supplementary]

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

David Wipf

[abs] [pdf]

Fast k-means with accurate bounds

James Newling, Francois Fleuret

[abs] [pdf] [supplementary]

Boolean Matrix Factorization and Noisy Completion via Message Passing

Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner

[abs] [pdf] [supplementary]

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Nadav Cohen, Amnon Shashua

[abs] [pdf] [supplementary]

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, Ben Recht

[abs] [pdf]

Anytime Exploration for Multi-armed Bandits using Confidence Information

Kwang-Sung Jun, Robert Nowak

[abs] [pdf] [supplementary]

Structured Prediction Energy Networks

David Belanger, Andrew McCallum

[abs] [pdf] [supplementary]

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time

Yuchen Zhang, Jason D. Lee, Michael I. Jordan

[abs] [pdf]

Compressive Spectral Clustering

Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst

[abs] [pdf] [supplementary]

Low-rank tensor completion: a Riemannian manifold preconditioning approach

Hiroyuki Kasai, Bamdev Mishra

[abs] [pdf] [supplementary]

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

Huishuai Zhang, Yuejie Chi, Yingbin Liang

[abs] [pdf] [supplementary]

Estimating Maximum Expected Value through Gaussian Approximation

Carlo D’Eramo, Marcello Restelli, Alessandro Nuara

[abs] [pdf] [supplementary]

Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak

[abs] [pdf] [supplementary]

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

Yarin Gal, Zoubin Ghahramani

[abs] [pdf] [supplementary]

Generative Adversarial Text to Image Synthesis

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee

[abs] [pdf] [supplementary]

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

Sandhya Prabhakaran, Elham Azizi, Ambrose Carr, Dana Pe’er

[abs] [pdf] [supplementary]

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

Zeyuan Allen-Zhu, Yang Yuan

[abs] [pdf]

Sparse Parameter Recovery from Aggregated Data

Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo

[abs] [pdf] [supplementary]

Deep Structured Energy Based Models for Anomaly Detection

Shuangfei Zhai, Yu Cheng, Weining Lu, Zhongfei Zhang

[abs] [pdf]

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling

Zeyuan Allen-Zhu, Zheng Qu, Peter Richtarik, Yang Yuan

[abs] [pdf]

Unitary Evolution Recurrent Neural Networks

Martin Arjovsky, Amar Shah, Yoshua Bengio

[abs] [pdf]

Markov Latent Feature Models

Aonan Zhang, John Paisley

[abs] [pdf]

The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

Yingfei Wang, Chu Wang, Warren Powell

[abs] [pdf] [supplementary]

A Simple and Provable Algorithm for Sparse Diagonal CCA

Megasthenis Asteris, Anastasios Kyrillidis, Oluwasanmi Koyejo, Russell Poldrack

[abs] [pdf] [supplementary]

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

Huikang Liu, Weijie Wu, Anthony Man-Cho So

[abs] [pdf] [supplementary]

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

Devansh Arpit, Yingbo Zhou, Bhargava Kota, Venu Govindaraju

[abs] [pdf] [supplementary]

Learning to Generate with Memory

Chongxuan Li, Jun Zhu, Bo Zhang

[abs] [pdf] [supplementary]

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

Basura Fernando, Stephen Gould

[abs] [pdf]

Learning to Filter with Predictive State Inference Machines

Wen Sun, Arun Venkatraman, Byron Boots, J.Andrew Bagnell

[abs] [pdf] [supplementary]

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

Mostafa Rahmani, Geroge Atia

[abs] [pdf]

DCM Bandits: Learning to Rank with Multiple Clicks

Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, Zheng Wen

[abs] [pdf] [supplementary]

Train faster, generalize better: Stability of stochastic gradient descent

Moritz Hardt, Ben Recht, Yoram Singer

[abs] [pdf]

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

Junpei Komiyama, Junya Honda, Hiroshi Nakagawa

[abs] [pdf] [supplementary]

Contextual Combinatorial Cascading Bandits

Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen

[abs] [pdf] [supplementary]

Conservative Bandits

Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvari

[abs] [pdf] [supplementary]

Variance-Reduced and Projection-Free Stochastic Optimization

Elad Hazan, Haipeng Luo

[abs] [pdf] [supplementary]

Factored Temporal Sigmoid Belief Networks for Sequence Learning

Jiaming Song, Zhe Gan, Lawrence Carin

[abs] [pdf] [supplementary]

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

QianQian Xu, Jiechao Xiong, Xiaochun Cao, Yuan Yao

[abs] [pdf] [supplementary]

Strongly-Typed Recurrent Neural Networks

David Balduzzi, Muhammad Ghifary

[abs] [pdf]

Distributed Clustering of Linear Bandits in Peer to Peer Networks

Nathan Korda, Balazs Szorenyi, Shuai Li

[abs] [pdf] [supplementary]

Collapsed Variational Inference for Sum-Product Networks

Han Zhao, Tameem Adel, Geoff Gordon, Brandon Amos

[abs] [pdf] [supplementary]

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

Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone

[abs] [pdf]

Benchmarking Deep Reinforcement Learning for Continuous Control

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

[abs] [pdf] [supplementary]

\(K\)-Means Clustering with Distributed Dimensions

Hu Ding, Yu Liu, Lingxiao Huang, Jian Li

[abs] [pdf] [supplementary]

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

Dmitry Ulyanov, Vadim Lebedev, Andrea, Victor Lempitsky

[abs] [pdf] [supplementary]

Fast Constrained Submodular Maximization: Personalized Data Summarization

Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi

[abs] [pdf] [supplementary]

On the Statistical Limits of Convex Relaxations

Zhaoran Wang, Quanquan Gu, Han Liu

[abs] [pdf] [supplementary]

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

[abs] [pdf] [supplementary]

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon

[abs] [pdf] [supplementary]

Solving Ridge Regression using Sketched Preconditioned SVRG

Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz

[abs] [pdf] [supplementary]

Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control

Prashanth L.A., Cheng Jie, Michael Fu, Steve Marcus, Csaba Szepesvari

[abs] [pdf]

Estimating Accuracy from Unlabeled Data: A Bayesian Approach

Emmanouil Antonios Platanios, Avinava Dubey, Tom Mitchell

[abs] [pdf] [supplementary]

Non-negative Matrix Factorization under Heavy Noise

Chiranjib Bhattacharya, Navin Goyal, Ravindran Kannan, Jagdeep Pani

[abs] [pdf] [supplementary]

Extreme F-measure Maximization using Sparse Probability Estimates

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

[abs] [pdf] [supplementary]

Auxiliary Deep Generative Models

Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther

[abs] [pdf]

Importance Sampling Tree for Large-scale Empirical Expectation

Olivier Canevet, Cijo Jose, Francois Fleuret

[abs] [pdf] [supplementary]

Starting Small - Learning with Adaptive Sample Sizes

Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann

[abs] [pdf] [supplementary]

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang Bui, Daniel Hernandez-Lobato, Jose miguel Hernandez-Lobato, Yingzhen Li, Richard Turner

[abs] [pdf] [supplementary]

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

Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh

[abs] [pdf]

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hernandez-Lobato, Jose miguel Hernandez-Lobato, Amar Shah, Ryan Adams

[abs] [pdf] [supplementary]

Rich Component Analysis

Rong Ge, James Zou

[abs] [pdf] [supplementary]

Black-Box Alpha Divergence Minimization

Jose miguel Hernandez-Lobato, Yingzhen Li, Mark Rowland, Thang Bui, Daniel Hernandez-Lobato, Richard Turner

[abs] [pdf] [supplementary]

One-Shot Generalization in Deep Generative Models

Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, Daan Wierstra

[abs] [pdf]

Optimal Classification with Multivariate Losses

Nagarajan Natarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, Inderjit Dhillon

[abs] [pdf] [supplementary]

A ranking approach to global optimization

Cedric Malherbe, Emile Contal, Nicolas Vayatis

[abs] [pdf] [supplementary]

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric Xing

[abs] [pdf] [supplementary]

Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther

[abs] [pdf]

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling

Christopher De Sa, Chris Re, Kunle Olukotun

[abs] [pdf] [supplementary]

Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi

[abs] [pdf] [supplementary]

Anytime optimal algorithms in stochastic multi-armed bandits

Rémy Degenne, Vianney Perchet

[abs] [pdf] [supplementary]

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

William Hoiles, Mihaela van der Schaar

[abs] [pdf] [supplementary]

On collapsed representation of hierarchical Completely Random Measures

Gaurav Pandey, Ambedkar Dukkipati

[abs] [pdf] [supplementary]

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

Andre Martins, Ramon Astudillo

[abs] [pdf] [supplementary]

Black-box Optimization with a Politician

Sebastien Bubeck, Yin Tat Lee

[abs] [pdf]

Gaussian process nonparametric tensor estimator and its minimax optimality

Heishiro Kanagawa, Taiji Suzuki, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami

[abs] [pdf] [supplementary]

No-Regret Algorithms for Heavy-Tailed Linear Bandits

Andres Munoz Medina, Scott Yang

[abs] [pdf]

Extended and Unscented Kitchen Sinks

Edwin Bonilla, Daniel Steinberg, Alistair Reid

[abs] [pdf] [supplementary]

Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization

Zhiqiang Xu, Peilin Zhao, Jianneng Cao, Xiaoli Li

[abs] [pdf] [supplementary]

Recommendations as Treatments: Debiasing Learning and Evaluation

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

[abs] [pdf] [supplementary]

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

Jinsung Yoon, Ahmed Alaa, Scott Hu, Mihaela van der Schaar

[abs] [pdf] [supplementary]

An optimal algorithm for the Thresholding Bandit Problem

Andrea Locatelli, Maurilio Gutzeit, Alexandra Carpentier

[abs] [pdf] [supplementary]

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching

Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier

[abs] [pdf]

Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

Christos Louizos, Max Welling

[abs] [pdf] [supplementary]

Learning Granger Causality for Hawkes Processes

Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha

[abs] [pdf] [supplementary]

Neural Variational Inference for Text Processing

Yishu Miao, Lei Yu, Phil Blunsom

[abs] [pdf] [supplementary]

Dictionary Learning for Massive Matrix Factorization

Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux

[abs] [pdf]

Pixel Recurrent Neural Networks

Aaron Van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu

[abs] [pdf]

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

Ozgur Simsek, Simon Algorta, Amit Kothiyal

[abs] [pdf]

Gaussian quadrature for matrix inverse forms with applications

Chengtao Li, Suvrit Sra, Stefanie Jegelka

[abs] [pdf] [supplementary]

Train and Test Tightness of LP Relaxations in Structured Prediction

Ofer Meshi, Mehrdad Mahdavi, Adrian Weller, David Sontag

[abs] [pdf] [supplementary]

Stochastic Optimization for Multiview Representation Learning using Partial Least Squares

Raman Arora, Poorya Mianjy, Teodor Marinov

[abs] [pdf] [supplementary]

Hierarchical Compound Poisson Factorization

Mehmet Basbug, Barbara Engelhardt

[abs] [pdf] [supplementary]

Opponent Modeling in Deep Reinforcement Learning

He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III

[abs] [pdf]

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

Xiangyu Wang, David Dunson, Chenlei Leng

[abs] [pdf] [supplementary]

SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization

Zheng Qu, Peter Richtarik, Martin Takac, Olivier Fercoq

[abs] [pdf] [supplementary]

On Graduated Optimization for Stochastic Non-Convex Problems

Elad Hazan, Kfir Yehuda Levy, Shai Shalev-Shwartz

[abs] [pdf] [supplementary]

Meta-Learning with Memory-Augmented Neural Networks

Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap

[abs] [pdf] [supplementary]

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

Ran Dai, Rina Barber

[abs] [pdf]

Softened Approximate Policy Iteration for Markov Games

Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin

[abs] [pdf] [supplementary]

Stochastic Block BFGS: Squeezing More Curvature out of Data

Robert Gower, Donald Goldfarb, Peter Richtarik

[abs] [pdf]

Differential Geometric Regularization for Supervised Learning of Classifiers

Qinxun Bai, Steven Rosenberg, Zheng Wu, Stan Sclaroff

[abs] [pdf] [supplementary]

Exploiting Cyclic Symmetry in Convolutional Neural Networks

Sander Dieleman, Jeffrey De Fauw, Koray Kavukcuoglu

[abs] [pdf]

Graying the black box: Understanding DQNs

Tom Zahavy, Nir Ben-Zrihem, Shie Mannor

[abs] [pdf] [supplementary]

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

Abram Friesen, Pedro Domingos

[abs] [pdf] [supplementary]

Pareto Frontier Learning with Expensive Correlated Objectives

Amar Shah, Zoubin Ghahramani

[abs] [pdf] [supplementary]

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

[abs] [pdf] [supplementary]

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

Nate Veldt, David Gleich, Michael Mahoney

[abs] [pdf] [supplementary]

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

[abs] [pdf] [supplementary]

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

Masanori Kawakita, Jun’ichi Takeuchi

[abs] [pdf] [supplementary]

Nonparametric Canonical Correlation Analysis

Tomer Michaeli, Weiran Wang, Karen Livescu

[abs] [pdf] [supplementary]

BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits

Alexander Rakhlin, Karthik Sridharan

[abs] [pdf] [supplementary]

Associative Long Short-Term Memory

Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves

[abs] [pdf] [supplementary]

Dueling Network Architectures for Deep Reinforcement Learning

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

[abs] [pdf]

Persistence weighted Gaussian kernel for topological data analysis

Genki Kusano, Yasuaki Hiraoka, Kenji Fukumizu

[abs] [pdf] [supplementary]

Learning Convolutional Neural Networks for Graphs

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

[abs] [pdf]

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

[abs] [pdf]

Recurrent Orthogonal Networks and Long-Memory Tasks

Mikael Henaff, Arthur Szlam, Yann LeCun

[abs] [pdf]

The Arrow of Time in Multivariate Time Series

Stefan Bauer, Bernhard Schölkopf, Jonas Peters

[abs] [pdf] [supplementary]

Mixture Proportion Estimation via Kernel Embeddings of Distributions

Harish Ramaswamy, Clayton Scott, Ambuj Tewari

[abs] [pdf] [supplementary]

Fast DPP Sampling for Nystrom with Application to Kernel Methods

Chengtao Li, Stefanie Jegelka, Suvrit Sra

[abs] [pdf] [supplementary]

Complex Embeddings for Simple Link Prediction

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

[abs] [pdf] [supplementary]

Interactive Bayesian Hierarchical Clustering

Sharad Vikram, Sanjoy Dasgupta

[abs] [pdf] [supplementary]

A Convolutional Attention Network for Extreme Summarization of Source Code

Miltiadis Allamanis, Hao Peng, Charles Sutton

[abs] [pdf]

How to Fake Multiply by a Gaussian Matrix

Michael Kapralov, Vamsi Potluru, David Woodruff

[abs] [pdf] [supplementary]

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

Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan

[abs] [pdf] [supplementary]

Pliable Rejection Sampling

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric Maillard

[abs] [pdf] [supplementary]

Differentially Private Policy Evaluation

Borja Balle, Maziar Gomrokchi, Doina Precup

[abs] [pdf] [supplementary]

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning

Philip Thomas, Emma Brunskill

[abs] [pdf] [supplementary]

Discrete Deep Feature Extraction: A Theory and New Architectures

Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Boelcskei

[abs] [pdf] [supplementary]

Efficient Algorithms for Adversarial Contextual Learning

Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire

[abs] [pdf] [supplementary]

Training Deep Neural Networks via Direct Loss Minimization

Yang Song, Alexander Schwing, Richard, Raquel Urtasun

[abs] [pdf] [supplementary]

Sequence to Sequence Training of CTC-RNNs with Partial Windowing

Kyuyeon Hwang, Wonyong Sung

[abs] [pdf] [supplementary]

Variational Inference for Monte Carlo Objectives

Andriy Mnih, Danilo Rezende

[abs] [pdf] [supplementary]

Hierarchical Decision Making In Electricity Grid Management

Gal Dalal, Elad Gilboa, Shie Mannor

[abs] [pdf]

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization

Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause, Yaron Singer

[abs] [pdf] [supplementary]

Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee

[abs] [pdf]

Isotonic Hawkes Processes

Yichen Wang, Bo Xie, Nan Du, Le Song

[abs] [pdf] [supplementary]

Cross-Graph Learning of Multi-Relational Associations

Hanxiao Liu, Yiming Yang

[abs] [pdf]

Markov-modulated Marked Poisson Processes for Check-in Data

Jiangwei Pan, Vinayak Rao, Pankaj Agarwal, Alan Gelfand

[abs] [pdf]

Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference

Tudor Achim, Ashish Sabharwal, Stefano Ermon

[abs] [pdf] [supplementary]

On the Power and Limits of Distance-Based Learning

Periklis Papakonstantinou, Jia Xu, Guang Yang

[abs] [pdf] [supplementary]

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

Ian En-Hsu Yen, Xin Lin, Jiong Zhang, Pradeep Ravikumar, Inderjit Dhillon

[abs] [pdf] [supplementary]

Generalized Direct Change Estimation in Ising Model Structure

Farideh Fazayeli, Arindam Banerjee

[abs] [pdf]

Robust Principal Component Analysis with Side Information

Kai-Yang Chiang, Cho-Jui Hsieh, Inderjit Dhillon

[abs] [pdf] [supplementary]

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

Huan Gui, Jiawei Han, Quanquan Gu

[abs] [pdf] [supplementary]

Early and Reliable Event Detection Using Proximity Space Representation

Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy

[abs] [pdf]

Stratified Sampling Meets Machine Learning

Edo Liberty, Kevin Lang, Konstantin Shmakov

[abs] [pdf]

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

[abs] [pdf] [supplementary]

Generalization Properties and Implicit Regularization for Multiple Passes SGM

Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco

[abs] [pdf] [supplementary]

Principal Component Projection Without Principal Component Analysis

Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford

[abs] [pdf] [supplementary]

Recovery guarantee of weighted low-rank approximation via alternating minimization

Yuanzhi Li, Yingyu Liang, Andrej Risteski

[abs] [pdf] [supplementary]

Deconstructing the Ladder Network Architecture

Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio

[abs] [pdf] [supplementary]

Generalization and Exploration via Randomized Value Functions

Ian Osband, Benjamin Van Roy, Zheng Wen

[abs] [pdf] [supplementary]

Evasion and Hardening of Tree Ensemble Classifiers

Alex Kantchelian, J. D. Tygar, Anthony Joseph

[abs] [pdf] [supplementary]

Dynamic Memory Networks for Visual and Textual Question Answering

Caiming Xiong, Stephen Merity, Richard Socher

[abs] [pdf]

Estimating Cosmological Parameters from the Dark Matter Distribution

Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

[abs] [pdf] [supplementary]

Learning Population-Level Diffusions with Generative RNNs

Tatsunori Hashimoto, David Gifford, Tommi Jaakkola

[abs] [pdf] [supplementary]

Expressiveness of Rectifier Networks

Xingyuan Pan, Vivek Srikumar

[abs] [pdf] [supplementary]

Discrete Distribution Estimation under Local Privacy

Peter Kairouz, Keith Bonawitz, Daniel Ramage

[abs] [pdf] [supplementary]

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

David Inouye, Pradeep Ravikumar, Inderjit Dhillon

[abs] [pdf] [supplementary]

A Box-Constrained Approach for Hard Permutation Problems

Cong Han Lim, Steve Wright

[abs] [pdf] [supplementary]

Geometric Mean Metric Learning

Pourya Zadeh, Reshad Hosseini, Suvrit Sra

[abs] [pdf]

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina Eldar, Tong Zhang

[abs] [pdf] [supplementary]

Conditional Bernoulli Mixtures for Multi-label Classification

Cheng Li, Bingyu Wang, Virgil Pavlu, Javed Aslam

[abs] [pdf] [supplementary]

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

Yutian Chen, Zoubin Ghahramani

[abs] [pdf] [supplementary]

Recycling Randomness with Structure for Sublinear time Kernel Expansions

Krzysztof Choromanski, Vikas Sindhwani

[abs] [pdf] [supplementary]

Bidirectional Helmholtz Machines

Jorg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio

[abs] [pdf]

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

Jacob Abernethy, Elad Hazan

[abs] [pdf] [supplementary]

Preconditioning Kernel Matrices

Kurt Cutajar, Michael Osborne, John Cunningham, Maurizio Filippone

[abs] [pdf] [supplementary]

Greedy Column Subset Selection: New Bounds and Distributed Algorithms

Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam

[abs] [pdf] [supplementary]

Dynamic Capacity Networks

Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron Courville

[abs] [pdf]

Pricing a Low-regret Seller

Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod

[abs] [pdf]

Estimation from Indirect Supervision with Linear Moments

Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang

[abs] [pdf] [supplementary]

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

Thomas Bottesch, Thomas Bühler, Markus Kächele

[abs] [pdf]

Learning and Inference via Maximum Inner Product Search

Stephen Mussmann, Stefano Ermon

[abs] [pdf] [supplementary]

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

Anton Rodomanov, Dmitry Kropotov

[abs] [pdf] [supplementary]

A Kernel Test of Goodness of Fit

Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

[abs] [pdf] [supplementary]

Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood

[abs] [pdf] [supplementary]

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

[abs] [pdf] [supplementary]

A Theory of Generative ConvNet

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

[abs] [pdf]

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity

Quanming Yao, James Kwok

[abs] [pdf]

Computationally Efficient Nyström Approximation using Fast Transforms

Si Si, Cho-Jui Hsieh, Inderjit Dhillon

[abs] [pdf] [supplementary]

Gromov-Wasserstein Averaging of Kernel and Distance Matrices

Gabriel Peyré, Marco Cuturi, Justin Solomon

[abs] [pdf]

Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics

Anirban Roychowdhury, Brian Kulis, Srinivasan Parthasarathy

[abs] [pdf] [supplementary]

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

[abs] [pdf] [supplementary]

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

Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev, Pavel Serdyukov

[abs] [pdf]

Discriminative Embeddings of Latent Variable Models for Structured Data

Hanjun Dai, Bo Dai, Le Song

[abs] [pdf] [supplementary]

Robust Random Cut Forest Based Anomaly Detection on Streams

Sudipto Guha, Nina Mishra, Gourav Roy, Okke Schrijvers

[abs] [pdf] [supplementary]

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

[abs] [pdf]

Clustering High Dimensional Categorical Data via Topographical Features

Chao Chen, Novi Quadrianto

[abs] [pdf]

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

Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford

[abs] [pdf] [supplementary]

Algorithms for Optimizing the Ratio of Submodular Functions

Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes

[abs] [pdf]

Model-Free Imitation Learning with Policy Optimization

Jonathan Ho, Jayesh Gupta, Stefano Ermon

[abs] [pdf] [supplementary]

ADIOS: Architectures Deep In Output Space

Moustapha Cisse, Maruan Al-Shedivat, Samy Bengio

[abs] [pdf]

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications

Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath

[abs] [pdf]

Control of Memory, Active Perception, and Action in Minecraft

Junhyuk Oh, Valliappa Chockalingam, Satinder, Honglak Lee

[abs] [pdf] [supplementary]

The Label Complexity of Mixed-Initiative Classifier Training

Jina Suh, Xiaojin Zhu, Saleema Amershi

[abs] [pdf] [supplementary]

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

Aaron Schein, Mingyuan Zhou, David Blei, Hanna Wallach

[abs] [pdf] [supplementary]

Tensor Decomposition via Joint Matrix Schur Decomposition

Nicolo Colombo, Nikos Vlassis

[abs] [pdf]

Continuous Deep Q-Learning with Model-based Acceleration

Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

[abs] [pdf] [supplementary]

Domain Adaptation with Conditional Transferable Components

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

[abs] [pdf] [supplementary]

Fixed Point Quantization of Deep Convolutional Networks

Darryl Lin, Sachin Talathi, Sreekanth Annapureddy

[abs] [pdf]

Provable Algorithms for Inference in Topic Models

Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra

[abs] [pdf]

Epigraph projections for fast general convex programming

Po-Wei Wang, Matt Wytock, Zico Kolter

[abs] [pdf] [supplementary]

Fast Algorithms for Segmented Regression

Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

[abs] [pdf] [supplementary]

Energetic Natural Gradient Descent

Philip Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill

[abs] [pdf] [supplementary]

Partition Functions from Rao-Blackwellized Tempered Sampling

David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

[abs] [pdf] [supplementary]

Learning Mixtures of Plackett-Luce Models

Zhibing Zhao, Peter Piech, Lirong Xia

[abs] [pdf] [supplementary]

Near Optimal Behavior via Approximate State Abstraction

David Abel, David Hershkowitz, Michael Littman

[abs] [pdf]

Power of Ordered Hypothesis Testing

Lihua Lei, William Fithian

[abs] [pdf] [supplementary]

PHOG: Probabilistic Model for Code

Pavol Bielik, Veselin Raychev, Martin Vechev

[abs] [pdf] [supplementary]

Shifting Regret, Mirror Descent, and Matrices

Andras Gyorgy, Csaba Szepesvari

[abs] [pdf]

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

Jelena Luketina, Tapani Raiko, Mathias Berglund, Klaus Greff

[abs] [pdf]

Model-Free Trajectory Optimization for Reinforcement Learning

Riad Akrour, Gerhard Neumann, Hany Abdulsamad, Abbas Abdolmaleki

[abs] [pdf] [supplementary]

Controlling the distance to a Kemeny consensus without computing it

Yunlong Jiao, Anna Korba, Eric Sibony

[abs] [pdf] [supplementary]

Horizontally Scalable Submodular Maximization

Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause

[abs] [pdf] [supplementary]

Group Equivariant Convolutional Networks

Taco Cohen, Max Welling

[abs] [pdf] [supplementary]

Stochastic Discrete Clenshaw-Curtis Quadrature

Nico Piatkowski, Katharina Morik

[abs] [pdf]

Correcting Forecasts with Multifactor Neural Attention

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

[abs] [pdf]

Learning Representations for Counterfactual Inference

Fredrik Johansson, Uri Shalit, David Sontag

[abs] [pdf] [supplementary]

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Yunseong Hwang, Anh Tong, Jaesik Choi

[abs] [pdf] [supplementary]

Inference Networks for Sequential Monte Carlo in Graphical Models

Brooks Paige, Frank Wood

[abs] [pdf] [supplementary]

Slice Sampling on Hamiltonian Trajectories

Benjamin Bloem-Reddy, John Cunningham

[abs] [pdf] [supplementary]

Noisy Activation Functions

Caglar Gulcehre, Marcin Moczulski, Misha Denil, Yoshua Bengio

[abs] [pdf]

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

[abs] [pdf] [supplementary]


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