ICML 2020

1,084 papers

Entropy Minimization In Emergent Languages

Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni

Feature Noise Induces Loss Discrepancy Across Groups

Fereshte Khani, Percy Liang

Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems

Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann

Quantum Expectation-Maximization for Gaussian mixture models

Iordanis Kerenidis, Alessandro Luongo, Anupam Prakash

Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations

Stephen Keeley, David Zoltowski, Yiyi Yu, Spencer Smith, Jonathan Pillow

Rate-distortion optimization guided autoencoder for isometric embedding in Euclidean latent space

Keizo Kato, Jing Zhou, Tomotake Sasaki, Akira Nakagawa

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, Francois Fleuret

Non-autoregressive Machine Translation with Disentangled Context Transformer

Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich

Operation-Aware Soft Channel Pruning using Differentiable Masks

Minsoo Kang, Bohyung Han

Learning and Evaluating Contextual Embedding of Source Code

Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, Kensen Shi

On the Power of Compressed Sensing with Generative Models

Akshay Kamath, Eric Price, Sushrut Karmalkar

Statistically Efficient Off-Policy Policy Gradients

Nathan Kallus, Masatoshi Uehara

Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation

Nathan Kallus, Masatoshi Uehara

DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

Nathan Kallus

Variational Autoencoders with Riemannian Brownian Motion Priors

Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, Soren Hauberg

Strategyproof Mean Estimation from Multiple-Choice Questions

Anson Kahng, Gregory Kehne, Ariel Procaccia

Partial Trace Regression and Low-Rank Kraus Decomposition

Hachem Kadri, Stéphane Ayache, Riikka Huusari, Alain Rakotomamonjy, Ralaivola Liva

Sub-Goal Trees a Framework for Goal-Based Reinforcement Learning

Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar

Distribution Augmentation for Generative Modeling

Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh

Sets Clustering

Ibrahim Jubran, Murad Tukan, Alaa Maalouf, Dan Feldman

A simpler approach to accelerated optimization: iterative averaging meets optimism

Pooria Joulani, Anant Raj, András György, Csaba Szepesvári

Stochastic Differential Equations with Variational Wishart Diffusions

Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni

Evaluating the Performance of Reinforcement Learning Algorithms

Scott Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip Thomas

Being Bayesian about Categorical Probability

Taejong Joo, Uijung Chung, Min-Gwan Seo

Fair k-Centers via Maximum Matching

Matthew Jones, Huy Nguyen, Thy Nguyen

On Relativistic f-Divergences

Alexia Jolicoeur-Martineau

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Rie Johnson, Tong Zhang

AdaScale SGD: A User-Friendly Algorithm for Distributed Training

Tyler Johnson, Pulkit Agrawal, Haijie Gu, Carlos Guestrin

Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model

Ying Jin, Zhaoran Wang, Junwei Lu

Efficiently Solving MDPs with Stochastic Mirror Descent

Yujia Jin, Aaron Sidford

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Chi Jin, Praneeth Netrapalli, Michael Jordan

Reward-Free Exploration for Reinforcement Learning

Chi Jin, Akshay Krishnamurthy, Max Simchowitz, Tiancheng Yu

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition

Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu

Multi-Objective Molecule Generation using Interpretable Substructures

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

Hierarchical Generation of Molecular Graphs using Structural Motifs

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola

Associative Memory in Iterated Overparameterized Sigmoid Autoencoders

Yibo Jiang, Cengiz Pehlevan

Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation

Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei

Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels

Lu Jiang, Di Huang, Mason Liu, Weilong Yang

BINOCULARS for efficient, nonmyopic sequential experimental design

Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett

Optimizing Black-box Metrics with Adaptive Surrogates

Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta

Information-Theoretic Local Minima Characterization and Regularization

Zhiwei Jia, Hao Su

History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms

Kaiyi Ji, Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang

T-GD: Transferable GAN-generated Images Detection Framework

Hyeonseong Jeon, Young Oh Bang, Junyaup Kim, Simon Woo

Extra-gradient with player sampling for faster convergence in n-player games

Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna

Source Separation with Deep Generative Priors

Vivek Jayaram, John Thickstun

Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

Daniel Jarrett, Mihaela van der Schaar

Parametric Gaussian Process Regressors

Martin Jankowiak, Geoff Pleiss, Jacob Gardner

Debiased Sinkhorn barycenters

Hicham Janati, Marco Cuturi, Alexandre Gramfort

Learning Portable Representations for High-Level Planning

Steven James, Benjamin Rosman, George Konidaris

Tails of Lipschitz Triangular Flows

Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker

Generalization to New Actions in Reinforcement Learning

Ayush Jain, Andrew Szot, Joseph Lim

Optimal Robust Learning of Discrete Distributions from Batches

Ayush Jain, Alon Orlitsky

Correlation Clustering with Asymmetric Classification Errors

Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev

Implicit Regularization of Random Feature Models

Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel

Semi-Supervised Learning with Normalizing Flows

Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson

Do We Need Zero Training Loss After Achieving Zero Training Error?

Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance

Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi

Linear Lower Bounds and Conditioning of Differentiable Games

Adam Ibrahim, Waı̈ss Azizian, Gauthier Gidel, Ioannis Mitliagkas

Meta-Learning with Shared Amortized Variational Inference

Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari

Multigrid Neural Memory

Tri Huynh, Michael Maire, Matthew Walter

Curvature-corrected learning dynamics in deep neural networks

Dongsung Huh

Dynamics of Deep Neural Networks and Neural Tangent Hierarchy

Jiaoyang Huang, Horng-Tzer Yau

Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

Hengguan Huang, Fuzhao Xue, Hao Wang, Ye Wang

Accelerated Stochastic Gradient-free and Projection-free Methods

Feihu Huang, Lue Tao, Songcan Chen

InstaHide: Instance-hiding Schemes for Private Distributed Learning

Yangsibo Huang, Zhao Song, Kai Li, Sanjeev Arora

Generating Programmatic Referring Expressions via Program Synthesis

Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi

More Information Supervised Probabilistic Deep Face Embedding Learning

Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang

Improving Transformer Optimization Through Better Initialization

Xiao Shi Huang, Felipe Perez, Jimmy Ba, Maksims Volkovs

Communication-Efficient Distributed PCA by Riemannian Optimization

Long-Kai Huang, Sinno Pan

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Wenlong Huang, Igor Mordatch, Deepak Pathak

Evaluating Lossy Compression Rates of Deep Generative Models

Sicong Huang, Alireza Makhzani, Yanshuai Cao, Roger Grosse

From Importance Sampling to Doubly Robust Policy Gradient

Jiawei Huang, Nan Jiang

Momentum-Based Policy Gradient Methods

Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang

XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation

Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat

“Other-Play” for Zero-Shot Coordination

Hengyuan Hu, Adam Lerer, Alex Peysakhovich, Jakob Foerster

The Non-IID Data Quagmire of Decentralized Machine Learning

Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip Gibbons

Infinite attention: NNGP and NTK for deep attention networks

Jiri Hron, Yasaman Bahri, Jascha Sohl-dickstein, Roman Novak

Lifted Disjoint Paths with Application in Multiple Object Tracking

Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda

Set Functions for Time Series

Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt

Learning Mixtures of Graphs from Epidemic Cascades

Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics

Matthew Hoffman, Yian Ma

Graph Filtration Learning

Christoph Hofer, Florian Graf, Bastian Rieck, Marc Niethammer, Roland Kwitt

Topologically Densified Distributions

Christoph Hofer, Florian Graf, Marc Niethammer, Roland Kwitt

Parameterized Rate-Distortion Stochastic Encoder

Quan Hoang, Trung Le, Dinh Phung

Learning Task-Agnostic Embedding of Multiple Black-Box Experts for Multi-Task Model Fusion

Nghia Hoang, Thanh Lam, Bryan Kian Hsiang Low, Patrick Jaillet

Optimizing Dynamic Structures with Bayesian Generative Search

Minh Hoang, Carleton Kingsford

Optimization and Analysis of the pAp@k Metric for Recommender Systems

Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain

Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

Fabian Hinder, André Artelt, Barbara Hammer

Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Joeri Hermans, Volodimir Begy, Gilles Louppe

Cost-Effective Interactive Attention Learning with Neural Attention Processes

Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization

Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis Bach, Laurent Massoulié

Minimax Rate for Learning From Pairwise Comparisons in the BTL Model

Julien Hendrickx, Alex Olshevsky, Venkatesh Saligrama

Data-Efficient Image Recognition with Contrastive Predictive Coding

Olivier Henaff

Gradient-free Online Learning in Continuous Games with Delayed Rewards

Amélie Heliou, Panayotis Mertikopoulos, Zhengyuan Zhou

Hierarchically Decoupled Imitation For Morphological Transfer

Donald Hejna, Lerrel Pinto, Pieter Abbeel

Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation

Reinhard Heckel, Mahdi Soltanolkotabi

The Tree Ensemble Layer: Differentiability meets Conditional Computation

Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder

Nested Subspace Arrangement for Representation of Relational Data

Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa

Contrastive Multi-View Representation Learning on Graphs

Kaveh Hassani, Amir Hosein Khasahmadi