ICML 2020

1,084 papers

Median Matrix Completion: from Embarrassment to Optimality

Weidong Liu, Xiaojun Mao, Raymond K. W. Wong

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks

Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu

A Chance-Constrained Generative Framework for Sequence Optimization

Xianggen Liu, Qiang Liu, Sen Song, Jian Peng

Hallucinative Topological Memory for Zero-Shot Visual Planning

Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits

Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar

An Imitation Learning Approach for Cache Replacement

Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

Yang Liu, Hongyi Guo

Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors

Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett

Boosting Deep Neural Network Efficiency with Dual-Module Inference

Liu Liu, Lei Deng, Zhaodong Chen, Yuke Wang, Shuangchen Li

Sparse Shrunk Additive Models

Guodong Liu, Hong Chen, Heng Huang

Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling

Yao Liu, Pierre-Luc Bacon, Emma Brunskill

Time-aware Large Kernel Convolutions

Vasileios Lioutas, Yuhong Guo

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games

Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos, Michael Jordan

Generalized and Scalable Optimal Sparse Decision Trees

Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer

Improving Generative Imagination in Object-Centric World Models

Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang

InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs

Zinan Lin, Kiran Thekumparampil, Giulia Fanti, Sewoong Oh

Handling the Positive-Definite Constraint in the Bayesian Learning Rule

Wu Lin, Mark Schmidt, Mohammad Emtiyaz Khan

On the Theoretical Properties of the Network Jackknife

Qiaohui Lin, Robert Lunde, Purnamrita Sarkar

Extrapolation for Large-batch Training in Deep Learning

Tao LIN, Lingjing Kong, Sebastian Stich, Martin Jaggi

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems

Tianyi Lin, Chi Jin, Michael Jordan

Hierarchical Verification for Adversarial Robustness

Cong Han Lim, Raquel Urtasun, Ersin Yumer

AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation

Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-Wei Huang

Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning

Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty

Variable Skipping for Autoregressive Range Density Estimation

Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

Jian Liang, Dapeng Hu, Jiashi Feng

On a projective ensemble approach to two sample test for equality of distributions

Zhimei Li, Yaowu Zhang

RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, Dejing Dou

Input-Sparsity Low Rank Approximation in Schatten Norm

Yi Li, David Woodruff

Temporal Logic Point Processes

Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu

Nearly Linear Row Sampling Algorithm for Quantile Regression

Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang

Almost Tune-Free Variance Reduction

Bingcong Li, Lingda Wang, Georgios B. Giannakis

Train Big, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers

Zhuohan Li, Eric Wallace, Sheng Shen, Kevin Lin, Kurt Keutzer

Evolutionary Topology Search for Tensor Network Decomposition

CHAO LI, Zhun Sun

Learning from Irregularly-Sampled Time Series: A Missing Data Perspective

Steven Cheng-Xian Li, Benjamin Marlin

Visual Grounding of Learned Physical Models

Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins

Latent Space Factorisation and Manipulation via Matrix Subspace Projection

Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

Jianing Li, Yanyan Lan, Jiafeng Guo, Xueqi Cheng

Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu

Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability

Mingjie Li, Lingshen He, Zhouchen Lin

PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Shiyu Li, Edward Hanson, Hai Li, Yiran Chen

Neural Architecture Search in A Proxy Validation Loss Landscape

Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu

Manifold Identification for Ultimately Communication-Efficient Distributed Optimization

Yu-Sheng Li, Wei-Lin Chiang, Ching-pei Lee

ACFlow: Flow Models for Arbitrary Conditional Likelihoods

Yang Li, Shoaib Akbar, Junier Oliva

Learning Quadratic Games on Networks

Yan Leng, Xiaowen Dong, Junfeng Wu, Alex Pentland

Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent

Yunwen Lei, Yiming Ying

SGD Learns One-Layer Networks in WGANs

Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis

Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks

Jiabao Lei, Kui Jia

Tensor denoising and completion based on ordinal observations

Chanwoo Lee, Miaoyan Wang

Temporal Phenotyping using Deep Predictive Clustering of Disease Progression

Changhee Lee, Mihaela van der Schaar

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, Jinwoo Shin

Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning

Sang-Hyun Lee, Seung-Woo Seo

Accelerated Message Passing for Entropy-Regularized MAP Inference

Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan

Batch Reinforcement Learning with Hyperparameter Gradients

Byungjun Lee, Jongmin Lee, Peter Vrancx, Dongho Kim, Kee-eung Kim

Self-supervised Label Augmentation via Input Transformations

Hankook Lee, Sung Ju Hwang, Jinwoo Shin

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel

Causal Effect Identifiability under Partial-Observability

Sanghack Lee, Elias Bareinboim

Self-Attentive Associative Memory

Hung Le, Truyen Tran, Svetha Venkatesh

Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization

Hien Le, Nicolas Gillis, Panagiotis Patrinos

Learning with Good Feature Representations in Bandits and in RL with a Generative Model

Tor Lattimore, Csaba Szepesvári, Gellert Weisz

Efficient Proximal Mapping of the 1-path-norm of Shallow Networks

Fabian Latorre, Paul Rolland, Nadav Hallak, Volkan Cevher

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Michael Laskin, Aravind Srinivas, Pieter Abbeel

Robust and Stable Black Box Explanations

Himabindu Lakkaraju, Nino Arsov, Osbert Bastani

Bidirectional Model-based Policy Optimization

Hang Lai, Jian Shen, Weinan Zhang, Yong Yu

Recht-Re Noncommutative Arithmetic-Geometric Mean Conjecture is False

Zehua Lai, Lek-Heng Lim

Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses

Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d’Alché-Buc

Optimal Randomized First-Order Methods for Least-Squares Problems

Jonathan Lacotte, Mert Pilanci

Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions

Prashanth L.A., Krishna Jagannathan, Ravi Kolla

Principled learning method for Wasserstein distributionally robust optimization with local perturbations

Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov

Soft Threshold Weight Reparameterization for Learnable Sparsity

Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain

Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks

Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr

Online Dense Subgraph Discovery via Blurred-Graph Feedback

Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama

Two Routes to Scalable Credit Assignment without Weight Symmetry

Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena, Surya Ganguli, Jonathan Bloom

Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

Daniel Kumor, Carlos Cinelli, Elias Bareinboim

Problems with Shapley-value-based explanations as feature importance measures

I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, Sorelle Friedler

On Implicit Regularization in $β$-VAEs

Abhishek Kumar, Ben Poole

Understanding Self-Training for Gradual Domain Adaptation

Ananya Kumar, Tengyu Ma, Percy Liang

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

Anurag Kumar, Vamsi Ithapu

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Asynchronous Coagent Networks

James Kostas, Chris Nota, Philip Thomas

On the Sample Complexity of Adversarial Multi-Source PAC Learning

Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates

Lingkai Kong, Jimeng Sun, Chao Zhang

Meta-learning for Mixed Linear Regression

Weihao Kong, Raghav Somani, Zhao Song, Sham Kakade, Sewoong Oh

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian Stich

Online Learning for Active Cache Synchronization

Andrey Kolobov, Sébastien Bubeck, Julian Zimmert

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities

Jonas Köhler, Leon Klein, Frank Noe

Georg Kohl, Kiwon Um, Nils Thuerey

Concept Bottleneck Models

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson

Optimal Continual Learning has Perfect Memory and is NP-hard

Jeremias Knoblauch, Hisham Husain, Tom Diethe

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Steven Kleinegesse, Michael U. Gutmann

Active World Model Learning with Progress Curiosity

Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

Variational Inference for Sequential Data with Future Likelihood Estimates

Geon-hyeong Kim, Youngsoo Jang, Hongseok Yang, Kee-eung Kim

Domain Adaptive Imitation Learning

Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon

Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup

Jang-Hyun Kim, Wonho Choo, Hyun Oh Song

FACT: A Diagnostic for Group Fairness Trade-offs

Joon Sik Kim, Jiahao Chen, Ameet Talwalkar

Uniform Convergence of Rank-weighted Learning

Justin Khim, Liu Leqi, Adarsh Prasad, Pradeep Ravikumar

What can I do here? A Theory of Affordances in Reinforcement Learning

Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup

Private Outsourced Bayesian Optimization

Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low