ICML 2020

1,084 papers

CoMic: Complementary Task Learning & Mimicry for Reusable Skills

Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel

Bayesian Graph Neural Networks with Adaptive Connection Sampling

Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield

A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits

Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu

Improving generalization by controlling label-noise information in neural network weights

Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin

Data Amplification: Instance-Optimal Property Estimation

Yi Hao, Alon Orlitsky

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

Filip Hanzely, Dmitry Kovalev, Peter Richtarik

Stochastic Subspace Cubic Newton Method

Filip Hanzely, Nikita Doikov, Yurii Nesterov, Peter Richtarik

Training Binary Neural Networks through Learning with Noisy Supervision

Kai Han, Yunhe Wang, Yixing Xu, Chunjing XU, Enhua Wu

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu

DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix

Insu Han, Haim Avron, Jinwoo Shin

FedBoost: A Communication-Efficient Algorithm for Federated Learning

Jenny Hamer, Mehryar Mohri, Ananda Theertha Suresh

Optimal approximation for unconstrained non-submodular minimization

Marwa El Halabi, Stefanie Jegelka

Let’s Agree to Agree: Neural Networks Share Classification Order on Real Datasets

Guy Hacohen, Leshem Choshen, Daphna Weinshall

Streaming Submodular Maximization under a k-Set System Constraint

Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi

Retrieval Augmented Language Model Pre-Training

Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, Mingwei Chang

Multidimensional Shape Constraints

Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Taman Narayan

Neural Topic Modeling with Continual Lifelong Learning

Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schuetze

Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data

Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-feng Li, Zhi-Hua Zhou

Accelerating Large-Scale Inference with Anisotropic Vector Quantization

Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha

Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning

Zhaohan Daniel Guo, Bernardo Avila Pires, Bilal Piot, Jean-Bastien Grill, Florent Altché

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu

Learning to Branch for Multi-Task Learning

Pengsheng Guo, Chen-Yu Lee, Daniel Ulbricht

LTF: A Label Transformation Framework for Correcting Label Shift

Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao

Certified Data Removal from Machine Learning Models

Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten

Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen

Recurrent Hierarchical Topic-Guided RNN for Language Generation

Dandan Guo, Bo Chen, Ruiying Lu, Mingyuan Zhou

Improving the Gating Mechanism of Recurrent Neural Networks

Albert Gu, Caglar Gulcehre, Thomas Paine, Matt Hoffman, Razvan Pascanu

Implicit Geometric Regularization for Learning Shapes

Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman

Near-Tight Margin-Based Generalization Bounds for Support Vector Machines

Allan Grønlund, Lior Kamma, Kasper Green Larsen

Monte-Carlo Tree Search as Regularized Policy Optimization

Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko

Robust Learning with the Hilbert-Schmidt Independence Criterion

Daniel Greenfeld, Uri Shalit

On the Iteration Complexity of Hypergradient Computation

Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel

Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase

Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski

DROCC: Deep Robust One-Class Classification

Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain

PackIt: A Virtual Environment for Geometric Planning

Ankit Goyal, Jia Deng

PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination

Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje, Venkatesan Chakaravarthy, Yogish Sabharwal

Ordinal Non-negative Matrix Factorization for Recommendation

Olivier Gouvert, Thomas Oberlin, Cédric Févotte

Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Celi

Automatic Reparameterisation of Probabilistic Programs

Maria Gorinova, Dave Moore, Matthew Hoffman

The continuous categorical: a novel simplex-valued exponential family

Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John Cunningham

Differentially Private Set Union

Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi

Towards a General Theory of Infinite-Width Limits of Neural Classifiers

Eugene Golikov

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova

SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification

Tomer Golany, Kira Radinsky, Daniel Freedman

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent

Surbhi Goel, Aravind Gollakota, Zhihan Jin, Sushrut Karmalkar, Adam Klivans

One Size Fits All: Can We Train One Denoiser for All Noise Levels?

Abhiram Gnanasambandam, Stanley Chan

Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition

Alex Gittens, Kareem Aggour, Bulent Yener

Representations for Stable Off-Policy Reinforcement Learning

Dibya Ghosh, Marc G. Bellemare

Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos

Subhroshekhar Ghosh, Krishna Balasubramanian, Xiaochuan Yang

A Distributional Framework For Data Valuation

Amirata Ghorbani, Michael Kim, James Zou

Gradient Temporal-Difference Learning with Regularized Corrections

Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White

Aligned Cross Entropy for Non-Autoregressive Machine Translation

Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs

AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang

Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics

Mahsa Ghasemi, Erdem Bulgur, Ufuk Topcu

Online Multi-Kernel Learning with Graph-Structured Feedback

Pouya M Ghari, Yanning Shen

Black-Box Methods for Restoring Monotonicity

Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos

Generalisation error in learning with random features and the hidden manifold model

Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mezard, Lenka Zdeborová

Multilinear Latent Conditioning for Generating Unseen Attribute Combinations

Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis

Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions

Sinong Geng, houssam nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar

Generalization and Representational Limits of Graph Neural Networks

Vikas Garg, Stefanie Jegelka, Tommi Jaakkola

Predicting deliberative outcomes

Vikas Garg, Tommi Jaakkola

Symbolic Network: Generalized Neural Policies for Relational MDPs

Sankalp Garg, Aniket Bajpai, Mausam

Online Convex Optimization in the Random Order Model

Dan Garber, Gal Korcia, Kfir Levy

Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?

Hongchang Gao, Heng Huang

A Free-Energy Principle for Representation Learning

Yansong Gao, Pratik Chaudhari

Abstraction Mechanisms Predict Generalization in Deep Neural Networks

Alex Gain, Hava Siegelmann

Stochastic bandits with arm-dependent delays

Manegueu Anne Gael, Claire Vernade, Alexandra Carpentier, Michal Valko

Accelerating the diffusion-based ensemble sampling by non-reversible dynamics

Futoshi Futami, Issei Sato, Masashi Sugiyama

Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions

Kaito Fujii

DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths

Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan Zeng

Don’t Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript

Fangcheng Fu, Yuzheng Hu, Yihan He, Jiawei Jiang, Yingxia Shao

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian

No-Regret and Incentive-Compatible Online Learning

Rupert Freeman, David Pennock, Chara Podimata, Jennifer Wortman Vaughan

Linear Mode Connectivity and the Lottery Ticket Hypothesis

Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

Leveraging Frequency Analysis for Deep Fake Image Recognition

Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa

Stochastic Latent Residual Video Prediction

Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen, Sylvain Lamprier, Patrick Gallinari

p-Norm Flow Diffusion for Local Graph Clustering

Kimon Fountoulakis, Di Wang, Shenghao Yang

Logarithmic Regret for Adversarial Online Control

Dylan Foster, Max Simchowitz

Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles

Dylan Foster, Alexander Rakhlin

Topic Modeling via Full Dependence Mixtures

Dan Fisher, Mark Kozdoba, Shie Mannor

Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains

Johannes Fischer, Omer Sahin Tas

Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data

Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization

Chris Finlay, Joern-Henrik Jacobsen, Levon Nurbekyan, Adam Oberman

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Angelos Filos, Panagiotis Tigkas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine

Implicit Learning Dynamics in Stackelberg Games: Equilibria Characterization, Convergence Analysis, and Empirical Study

Tanner Fiez, Benjamin Chasnov, Lillian Ratliff

Why Are Learned Indexes So Effective?

Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra

Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data

Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton

Accountable Off-Policy Evaluation With Kernel Bellman Statistics

Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu

The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation

Zhe Feng, David Parkes, Haifeng Xu

Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models

Yiding Feng, Ekaterina Khmelnitskaya, Denis Nekipelov

Learning with Multiple Complementary Labels

Lei Feng, Takuo Kaneko, Bo Han, Gang Niu, Bo An

Revisiting Fundamentals of Experience Replay

William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle

Improved Optimistic Algorithms for Logistic Bandits

Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq

Growing Action Spaces

Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier