ICML 2019

773 papers

Graph Resistance and Learning from Pairwise Comparisons

Julien Hendrickx, Alexander Olshevsky, Venkatesh Saligrama

On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

Hoda Heidari, Vedant Nanda, Krishna Gummadi

Provably Efficient Maximum Entropy Exploration

Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest

On the Impact of the Activation function on Deep Neural Networks Training

Soufiane Hayou, Arnaud Doucet, Judith Rousseau

Understanding and Controlling Memory in Recurrent Neural Networks

Doron Haviv, Alexander Rivkind, Omri Barak

Submodular Observation Selection and Information Gathering for Quadratic Models

Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu

Per-Decision Option Discounting

Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowe, Doina Precup

Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications

Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi

Random Shuffling Beats SGD after Finite Epochs

Jeff Haochen, Suvrit Sra

Doubly-Competitive Distribution Estimation

Yi Hao, Alon Orlitsky

Importance Sampling Policy Evaluation with an Estimated Behavior Policy

Josiah Hanna, Scott Niekum, Peter Stone

Complexity of Linear Regions in Deep Networks

Boris Hanin, David Rolnick

Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning

Seungyul Han, Youngchul Sung

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

Lei Han, peng sun, Yali Du, Jiechao Xiong, Qing Wang

Neural Separation of Observed and Unobserved Distributions

Tavi Halperin, Ariel Ephrat, Yedid Hoshen

Learning Latent Dynamics for Planning from Pixels

Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha

Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization

Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck Cadambe

On The Power of Curriculum Learning in Training Deep Networks

Guy Hacohen, Daphna Weinshall

IMEXnet A Forward Stable Deep Neural Network

Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto

Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

Albert Gural, Boris Murmann

Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

Lingbing Guo, Zequn Sun, Wei Hu

Exploring interpretable LSTM neural networks over multi-variable data

Tian Guo, Tao LIN, Nino Antulov-Fantulin

Simple Black-box Adversarial Attacks

Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger

Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai

An Investigation of Model-Free Planning

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière

Towards a Deep and Unified Understanding of Deep Neural Models in NLP

Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He

Fast Algorithm for Generalized Multinomial Models with Ranking Data

Jiaqi Gu, Guosheng Yin

Graphite: Iterative Generative Modeling of Graphs

Aditya Grover, Aaron Zweig, Stefano Ermon

Multi-Object Representation Learning with Iterative Variational Inference

Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess

Learning to Optimize Multigrid PDE Solvers

Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel

Automatic Posterior Transformation for Likelihood-Free Inference

David Greenberg, Marcel Nonnenmacher, Jakob Macke

A Statistical Investigation of Long Memory in Language and Music

Alexander Greaves-Tunnell, Zaïd Harchaoui

Adaptive Sensor Placement for Continuous Spaces

James Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David Leslie, Sattar Vakili

Counterfactual Visual Explanations

Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh

Combining parametric and nonparametric models for off-policy evaluation

Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-velez

Obtaining Fairness using Optimal Transport Theory

Paula Gordaliza, Eustasio del Barrio, Gamboa Fabrice, Jean-Michel Loubes

Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization

Chengyue Gong, Jian Peng, Qiang Liu

Efficient Training of BERT by Progressively Stacking

Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang

The information-theoretic value of unlabeled data in semi-supervised learning

Alexander Golovnev, Dávid Pál, Balázs Szörényi

Online Algorithms for Rent-Or-Buy with Expert Advice

Sreenivas Gollapudi, Debmalya Panigrahi

Amortized Monte Carlo Integration

Adam Golinski, Frank Wood, Tom Rainforth

Estimating Information Flow in Deep Neural Networks

Ziv Goldfeld, Ewout Van Den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen

Discovering Conditionally Salient Features with Statistical Guarantees

Jaime Roquero Gimenez, James Zou

Adversarial Examples Are a Natural Consequence of Test Error in Noise

Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk

Learning to Groove with Inverse Sequence Transformations

Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes

Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii

Efficient Dictionary Learning with Gradient Descent

Dar Gilboa, Sam Buchanan, John Wright

Data Shapley: Equitable Valuation of Data for Machine Learning

Amirata Ghorbani, James Zou

An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

Behrooz Ghorbani, Shankar Krishnan, Ying Xiao

An Instability in Variational Inference for Topic Models

Behrooz Ghorbani, Hamid Javadi, Andrea Montanari

Recursive Sketches for Modular Deep Learning

Badih Ghazi, Rina Panigrahy, Joshua Wang

Improved Parallel Algorithms for Density-Based Network Clustering

Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrovic

Learning and Data Selection in Big Datasets

Hossein Shokri Ghadikolaei, Hadi Ghauch, Carlo Fischione, Mikael Skoglund

Partially Linear Additive Gaussian Graphical Models

Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo

DeepMDP: Learning Continuous Latent Space Models for Representation Learning

Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

A Theory of Regularized Markov Decision Processes

Matthieu Geist, Bruno Scherrer, Olivier Pietquin

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

Yonatan Geifman, Ran El-Yaniv

Optimal Mini-Batch and Step Sizes for SAGA

Nidham Gazagnadou, Robert Gower, Joseph Salmon

Multi-Frequency Phase Synchronization

Tingran Gao, Zhizhen Zhao

Geometric Scattering for Graph Data Analysis

Feng Gao, Guy Wolf, Matthew Hirn

Demystifying Dropout

Hongchang Gao, Jian Pei, Heng Huang

Rate Distortion For Model Compression:From Theory To Practice

Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh

Deep Generative Learning via Variational Gradient Flow

Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang

Graph U-Nets

Hongyang Gao, Shuiwang Ji

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities

Octavian Ganea, Sylvain Gelly, Gary Becigneul, Aliaksei Severyn

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

Shani Gamrian, Yoav Goldberg

Off-Policy Deep Reinforcement Learning without Exploration

Scott Fujimoto, David Meger, Doina Precup

Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio

Kaito Fujii, Shinsaku Sakaue

MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement

Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin

Diagnosing Bottlenecks in Deep Q-learning Algorithms

Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton

Fast and Flexible Inference of Joint Distributions from their Marginals

Charlie Frogner, Tomaso Poggio

Approximating Orthogonal Matrices with Effective Givens Factorization

Thomas Frerix, Joan Bruna

Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar

Learning Discrete Structures for Graph Neural Networks

Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

On discriminative learning of prediction uncertainty

Vojtěch Franc, Daniel Průša

Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

Edwin Fong, Simon Lyddon, Chris Holmes

Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Jakob Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning

DL2: Training and Querying Neural Networks with Logic

Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang

Online Meta-Learning

Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine

Almost surely constrained convex optimization

Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher

Decentralized Exploration in Multi-Armed Bandits

Raphaël Féraud, Reda Alami, Romain Laroche

The advantages of multiple classes for reducing overfitting from test set reuse

Vitaly Feldman, Roy Frostig, Moritz Hardt

Invariant-Equivariant Representation Learning for Multi-Class Data

Ilya Feige

Dead-ends and Secure Exploration in Reinforcement Learning

Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen, Samira Ebrahimi Kahou

Regret Circuits: Composability of Regret Minimizers

Gabriele Farina, Christian Kroer, Tuomas Sandholm

Stable-Predictive Optimistic Counterfactual Regret Minimization

Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm

Multi-Frequency Vector Diffusion Maps

Yifeng Fan, Zhizhen Zhao

Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity

Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam

On the Connection Between Adversarial Robustness and Saliency Map Interpretability

Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schoenlieb

Cross-Domain 3D Equivariant Image Embeddings

Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia

Exploring the Landscape of Spatial Robustness

Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

Improved Convergence for ℓ_1 and ℓ_∞ Regression via Iteratively Reweighted Least Squares

Alina Ene, Adrian Vladu

Sequential Facility Location: Approximate Submodularity and Greedy Algorithm

Ehsan Elhamifar

GDPP: Learning Diverse Generations using Determinantal Point Processes

Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny

Semi-Cyclic Stochastic Gradient Descent

Hubert Eichner, Tomer Koren, Brendan Mcmahan, Nathan Srebro, Kunal Talwar

Imitating Latent Policies from Observation

Ashley Edwards, Himanshu Sahni, Yannick Schroecker, Charles Isbell

Band-limited Training and Inference for Convolutional Neural Networks

Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron Elmore, Michael Franklin

Autoregressive Energy Machines

Charlie Nash, Conor Durkan

Learning interpretable continuous-time models of latent stochastic dynamical systems

Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani