ICML 2019

773 papers

On the statistical rate of nonlinear recovery in generative models with heavy-tailed data

Xiaohan Wei, Zhuoran Yang, Zhaoran Wang

Generalized Linear Rule Models

Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Gunluk

AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes

Rachel Ward, Xiaoxia Wu, Léon Bottou

Jumpout : Improved Dropout for Deep Neural Networks with ReLUs

Shengjie Wang, Tianyi Zhou, Jeff Bilmes

Bias Also Matters: Bias Attribution for Deep Neural Network Explanation

Shengjie Wang, Tianyi Zhou, Jeff Bilmes

On the Generalization Gap in Reparameterizable Reinforcement Learning

Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher

Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random

Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi

On Sparse Linear Regression in the Local Differential Privacy Model

Di Wang, Jinhui Xu

Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions

Hao Wang, Berk Ustun, Flavio Calmon

Deep Factors for Forecasting

Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster

State-Regularized Recurrent Neural Networks

Cheng Wang, Mathias Niepert

On the Convergence and Robustness of Adversarial Training

Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou

Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models

Dilin Wang, Qiang Liu

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Chaoqi Wang, Roger Grosse, Sanja Fidler, Guodong Zhang

Improving Neural Language Modeling via Adversarial Training

Dilin Wang, Chengyue Gong, Qiang Liu

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Po-Wei Wang, Priya Donti, Bryan Wilder, Zico Kolter

Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation

Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, Yiannis Demiris

Differentially Private Empirical Risk Minimization with Non-convex Loss Functions

Di Wang, Changyou Chen, Jinhui Xu

Convolutional Poisson Gamma Belief Network

Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou

Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute

Tong Wang

Graph Convolutional Gaussian Processes

Ian Walker, Ben Glocker

On the Limitations of Representing Functions on Sets

Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne

Learning to select for a predefined ranking

Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev, Pavel Serdyukov

On the Design of Estimators for Bandit Off-Policy Evaluation

Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou, Tony Jebara

Understanding Priors in Bayesian Neural Networks at the Unit Level

Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel

Maximum Likelihood Estimation for Learning Populations of Parameters

Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade

Manifold Mixup: Better Representations by Interpolating Hidden States

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas

Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering

Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra

Learning Dependency Structures for Weak Supervision Models

Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Re

Model Comparison for Semantic Grouping

Francisco Vargas, Kamen Brestnichki, Nils Hammerla

Composing Value Functions in Reinforcement Learning

Benjamin Van Niekerk, Steven James, Adam Earle, Benjamin Rosman

Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD

Marten van Dijk, Lam Nguyen, PHUONG HA NGUYEN, Dzung Phan

Large-Scale Sparse Kernel Canonical Correlation Analysis

Viivi Uurtio, Sahely Bhadra, Juho Rousu

Fairness without Harm: Decoupled Classifiers with Preference Guarantees

Berk Ustun, Yang Liu, David Parkes

Sublinear Space Private Algorithms Under the Sliding Window Model

Jalaj Upadhyay

Distributed, Egocentric Representations of Graphs for Detecting Critical Structures

Ruo-Chun Tzeng, Shan-Hung Wu

Metropolis-Hastings Generative Adversarial Networks

Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski

Homomorphic Sensing

Manolis Tsakiris, Liangzu Peng

Learning Hawkes Processes Under Synchronization Noise

William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations

Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick

DeepNose: Using artificial neural networks to represent the space of odorants

Ngoc Tran, Daniel Kepple, Sergey Shuvaev, Alexei Koulakov

Bayesian Generative Active Deep Learning

Toan Tran, Thanh-Toan Do, Ian Reid, Gustavo Carneiro

Discovering Latent Covariance Structures for Multiple Time Series

Anh Tong, Jaesik Choi

Optimal Transport for structured data with application on graphs

Vayer Titouan, Nicolas Courty, Romain Tavenard, Chapel Laetitia, Rémi Flamary

Transfer of Samples in Policy Search via Multiple Importance Sampling

Andrea Tirinzoni, Mattia Salvini, Marcello Restelli

Random Matrix Improved Covariance Estimation for a Large Class of Metrics

Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac

ELF OpenGo: an analysis and open reimplementation of AlphaZero

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen

Combating Label Noise in Deep Learning using Abstention

Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof

Concentration Inequalities for Conditional Value at Risk

Philip Thomas, Erik Learned-Miller

Action Robust Reinforcement Learning and Applications in Continuous Control

Chen Tessler, Yonathan Efroni, Shie Mannor

Kernel Normalized Cut: a Theoretical Revisit

Yoshikazu Terada, Michio Yamamoto

The Natural Language of Actions

Guy Tennenholtz, Shie Mannor

Predicate Exchange: Inference with Declarative Knowledge

Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath

Variational Annealing of GANs: A Langevin Perspective

Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen

Adaptive Neural Trees

Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori

DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression

Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu

The Variational Predictive Natural Gradient

Da Tang, Rajesh Ranganath

Correlated Variational Auto-Encoders

Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi

Mallows ranking models: maximum likelihood estimate and regeneration

Wenpin Tang

Hierarchical Decompositional Mixtures of Variational Autoencoders

Ping Liang Tan, Robert Peharz

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Mingxing Tan, Quoc Le

Making Deep Q-learning methods robust to time discretization

Corentin Tallec, Léonard Blier, Yann Ollivier

Equivariant Transformer Networks

Kai Sheng Tai, Peter Bailis, Gregory Valiant

Accelerated Flow for Probability Distributions

Amirhossein Taghvaei, Prashant Mehta

Hyperbolic Disk Embeddings for Directed Acyclic Graphs

Ryota Suzuki, Ryusuke Takahama, Shun Onoda

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Raphael Suter, Djordje Miladinovic, Bernhard Schölkopf, Stefan Bauer

Active Learning for Decision-Making from Imbalanced Observational Data

Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria

Provably Efficient Imitation Learning from Observation Alone

Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell

Contextual Memory Trees

Wen Sun, Alina Beygelzimer, Hal Daume III, John Langford, Paul Mineiro

Learning Distance for Sequences by Learning a Ground Metric

Bing Su, Ying Wu

CAB: Continuous Adaptive Blending for Policy Evaluation and Learning

Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims

Learning Optimal Linear Regularizers

Matthew Streeter

BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

Asa Cooper Stickland, Iain Murray

Insertion Transformer: Flexible Sequence Generation via Insertion Operations

Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit

Faster Attend-Infer-Repeat with Tractable Probabilistic Models

Karl Stelzner, Robert Peharz, Kristian Kersting

Escaping Saddle Points with Adaptive Gradient Methods

Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

Compressing Gradient Optimizers via Count-Sketches

Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava

Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication

Pedro Soto, Jun Li, Xiaodi Fan

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-yan Liu

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

Zhao Song, Ron Parr, Lawrence Carin

SELFIE: Refurbishing Unclean Samples for Robust Deep Learning

Hwanjun Song, Minseok Kim, Jae-Gil Lee

Distribution calibration for regression

Hao Song, Tom Diethe, Meelis Kull, Peter Flach

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, Yung Yi

The Evolved Transformer

David So, Quoc Le, Chen Liang

GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Edward Smith, Scott Fujimoto, Adriana Romero, David Meger

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection

Lotfi Slim, Clement Chatelain, Chloe-Agathe Azencott, Jean-philippe Vert

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi

Non-Parametric Priors For Generative Adversarial Networks

Rajhans Singh, Pavan Turaga, Suren Jayasuriya, Ravi Garg, Martin Braun

A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks

Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban

Refined Complexity of PCA with Outliers

Kirill Simonov, Fedor Fomin, Petr Golovach, Fahad Panolan

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-paz

Revisiting precision recall definition for generative modeling

Loic Simon, Ryan Webster, Julien Rabin

Rehashing Kernel Evaluation in High Dimensions

Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis

Model-Based Active Exploration

Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez

Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning

Weishi Shi, Qi Yu

Scalable Training of Inference Networks for Gaussian-Process Models

Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu

Replica Conditional Sequential Monte Carlo

Alex Shestopaloff, Arnaud Doucet

Learning with Bad Training Data via Iterative Trimmed Loss Minimization

Yanyao Shen, Sujay Sanghavi

Hessian Aided Policy Gradient

Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi

Mixture Models for Diverse Machine Translation: Tricks of the Trade

Tianxiao Shen, Myle Ott, Michael Auli, Marc’Aurelio Ranzato