ICML 2017

434 papers

Robust Budget Allocation via Continuous Submodular Functions

Matthew Staib, Stefanie Jegelka

High-Dimensional Structured Quantile Regression

Vidyashankar Sivakumar, Arindam Banerjee

Nonparanormal Information Estimation

Shashank Singh, Barnabás Póczos

Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo

Umut Simsekli

The Predictron: End-To-End Learning and Planning

David Silver, Hado Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez

Gradient Boosted Decision Trees for High Dimensional Sparse Output

Si Si, Huan Zhang, S. Sathiya Keerthi, Dhruv Mahajan, Inderjit S. Dhillon

Attentive Recurrent Comparators

Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati

Bottleneck Conditional Density Estimation

Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh

Optimal Densification for Fast and Accurate Minwise Hashing

Anshumali Shrivastava

Learning Important Features Through Propagating Activation Differences

Avanti Shrikumar, Peyton Greenside, Anshul Kundaje

World of Bits: An Open-Domain Platform for Web-Based Agents

Tianlin Shi, Andrej Karpathy, Linxi Fan, Jonathan Hernandez, Percy Liang

GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization

Li Shen, Wei Liu, Ganzhao Yuan, Shiqian Ma

On the Iteration Complexity of Support Recovery via Hard Thresholding Pursuit

Jie Shen, Ping Li

Differentially Private Ordinary Least Squares

Or Sheffet

Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use

Vatsal Sharan, Gregory Valiant

Online Learning with Local Permutations and Delayed Feedback

Ohad Shamir, Liran Szlak

Estimating individual treatment effect: generalization bounds and algorithms

Uri Shalit, Fredrik D. Johansson, David Sontag

Failures of Gradient-Based Deep Learning

Shai Shalev-shwartz, Ohad Shamir, Shaked Shammah

Identifying Best Interventions through Online Importance Sampling

Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai

Developing Bug-Free Machine Learning Systems With Formal Mathematics

Daniel Selsam, Percy Liang, David L. Dill

Adapting Kernel Representations Online Using Submodular Maximization

Matthew Schlegel, Yangchen Pan, Jiecao Chen, Martha White

Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks

Kevin Scaman, Francis Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié

Hierarchy Through Composition with Multitask LMDPs

Andrew M. Saxe, Adam C. Earle, Benjamin Rosman

Analytical Guarantees on Numerical Precision of Deep Neural Networks

Charbel Sakr, Yongjune Kim, Naresh Shanbhag

Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data

Tomoya Sakai, Marthinus Christoffel Plessis, Gang Niu, Masashi Sugiyama

Asymmetric Tri-training for Unsupervised Domain Adaptation

Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada

Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks

Itay Safran, Ohad Shamir

Bayesian Boolean Matrix Factorisation

Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau

Enumerating Distinct Decision Trees

Salvatore Ruggieri

Pain-Free Random Differential Privacy with Sensitivity Sampling

Benjamin I. P. Rubinstein, Francesco Aldà

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick

Active Learning for Accurate Estimation of Linear Models

Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric

Real-Time Adaptive Image Compression

Oren Rippel, Lubomir Bourdev

Parallel Multiscale Autoregressive Density Estimation

Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang

Large-Scale Evolution of Image Classifiers

Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu

Equivariance Through Parameter-Sharing

Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos

High Dimensional Bayesian Optimization with Elastic Gaussian Process

Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh

Innovation Pursuit: A New Approach to the Subspace Clustering Problem

Mostafa Rahmani, George Atia

Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery

Mostafa Rahmani, George Atia

Estimating the unseen from multiple populations

Aditi Raghunathan, Gregory Valiant, James Zou

On the Expressive Power of Deep Neural Networks

Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, Jascha Sohl-dickstein

Online and Linear-Time Attention by Enforcing Monotonic Alignments

Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck

Neural Episodic Control

Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals

Robust Adversarial Reinforcement Learning

Lerrel Pinto, James Davidson, Rahul Sukthankar, Abhinav Gupta

Multi-task Learning with Labeled and Unlabeled Tasks

Anastasia Pentina, Christoph H. Lampert

Geometry of Neural Network Loss Surfaces via Random Matrix Theory

Jeffrey Pennington, Yasaman Bahri

Asynchronous Distributed Variational Gaussian Process for Regression

Hao Peng, Shandian Zhe, Xiao Zhang, Yuan Qi

Curiosity-driven Exploration by Self-supervised Prediction

Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell

Clustering by Sum of Norms: Stochastic Incremental Algorithm, Convergence and Cluster Recovery

Ashkan Panahi, Devdatt Dubhashi, Fredrik D. Johansson, Chiranjib Bhattacharyya

Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control

Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou, Byron Boots

A Birth-Death Process for Feature Allocation

Konstantina Palla, David Knowles, Zoubin Ghahramani

Stochastic Bouncy Particle Sampler

Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski

Dictionary Learning Based on Sparse Distribution Tomography

Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran, Michael Unser

Count-Based Exploration with Neural Density Models

Georg Ostrovski, Marc G. Bellemare, Aäron Oord, Rémi Munos

Bidirectional Learning for Time-series Models with Hidden Units

Takayuki Osogami, Hiroshi Kajino, Taro Sekiyama

Why is Posterior Sampling Better than Optimism for Reinforcement Learning?

Ian Osband, Benjamin Van Roy

Algebraic Variety Models for High-Rank Matrix Completion

Greg Ongie, Rebecca Willett, Robert D. Nowak, Laura Balzano

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

Shayegan Omidshafiei, Jason Pazis, Christopher Amato, Jonathan P. How, John Vian

The Statistical Recurrent Unit

Junier B. Oliva, Barnabás Póczos, Jeff Schneider

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning

Junhyuk Oh, Satinder Singh, Honglak Lee, Pushmeet Kohli

Nyström Method with Kernel K-means++ Samples as Landmarks

Dino Oglic, Thomas Gärtner

Conditional Image Synthesis with Auxiliary Classifier GANs

Augustus Odena, Christopher Olah, Jonathon Shlens

Multichannel End-to-end Speech Recognition

Tsubasa Ochiai, Shinji Watanabe, Takaaki Hori, John R. Hershey

Composing Tree Graphical Models with Persistent Homology Features for Clustering Mixed-Type Data

Xiuyan Ni, Novi Quadrianto, Yusu Wang, Chao Chen

SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takac

The Loss Surface of Deep and Wide Neural Networks

Quynh Nguyen, Matthias Hein

Post-Inference Prior Swapping

Willie Neiswanger, Eric Xing

Delta Networks for Optimized Recurrent Network Computation

Daniel Neil, Jun Haeng Lee, Tobi Delbruck, Shih-Chii Liu

Adaptive Sampling Probabilities for Non-Smooth Optimization

Hongseok Namkoong, Aman Sinha, Steve Yadlowsky, John C. Duchi

Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition

Tasha Nagamine, Nima Mesgarani

Meta Networks

Tsendsuren Munkhdalai, Hong Yu

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds

Mahesh Chandra Mukkamala, Matthias Hein

Sequence to Better Sequence: Continuous Revision of Combinatorial Structures

Jonas Mueller, David Gifford, Tommi Jaakkola

McGan: Mean and Covariance Feature Matching GAN

Youssef Mroueh, Tom Sercu, Vaibhava Goel

Coupling Distributed and Symbolic Execution for Natural Language Queries

Lili Mou, Zhengdong Lu, Hang Li, Zhi Jin

Regularising Non-linear Models Using Feature Side-information

Amina Mollaysa, Pablo Strasser, Alexandros Kalousis

Variational Dropout Sparsifies Deep Neural Networks

Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

Active Learning for Top-K Rank Aggregation from Noisy Comparisons

Soheil Mohajer, Changho Suh, Adel Elmahdy

Differentially Private Submodular Maximization: Data Summarization in Disguise

Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi

Improving Gibbs Sampler Scan Quality with DoGS

Ioannis Mitliagkas, Lester Mackey

Prediction and Control with Temporal Segment Models

Nikhil Mishra, Pieter Abbeel, Igor Mordatch

Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”

Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause

Tight Bounds for Approximate Carathéodory and Beyond

Vahab Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen

Variational Boosting: Iteratively Refining Posterior Approximations

Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams

Discovering Discrete Latent Topics with Neural Variational Inference

Yishu Miao, Edward Grefenstette, Phil Blunsom

Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections

Zakaria Mhammedi, Andrew Hellicar, Ashfaqur Rahman, James Bailey

Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Lars Mescheder, Sebastian Nowozin, Andreas Geiger

Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates

Jiali Mei, Yohann De Castro, Yannig Goude, Georges Hébrail

Risk Bounds for Transferring Representations With and Without Fine-Tuning

Daniel McNamara, Maria-Florina Balcan

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

Mason McGill, Pietro Perona

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Lucas Maystre, Matthias Grossglauser

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Lucas Maystre, Matthias Grossglauser

Bayesian Models of Data Streams with Hierarchical Power Priors

Andres Masegosa, Thomas D. Nielsen, Helge Langseth, Darı́o Ramos-López, Antonio Salmerón

On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations

Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti

Global optimization of Lipschitz functions

Cedric Malherbe, Nicolas Vayatis

Frame-based Data Factorizations

Sebastian Mair, Ahcène Boubekki, Ulf Brefeld

A Laplacian Framework for Option Discovery in Reinforcement Learning

Marlos C. Machado, Marc G. Bellemare, Michael Bowling

Interactive Learning from Policy-Dependent Human Feedback

James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang

Self-Paced Co-training

Fan Ma, Deyu Meng, Qi Xie, Zina Li, Xuanyi Dong