ICML 是 International Conference on Machine Learning的缩写，即国际机器学习大会。ICML如今已发展为由国际机器学习学会（IMLS）主办的年度机器学习国际顶级会议。
今年的ICML2020会议由于受疫情的影响改成了线上会议，做为人工智能领域的顶级会议之一，今年入选的论文一共1088篇，入选论文的数量创造了历史之最，但接受率却只有21.8%，低于2019年22.6%和2018年的24.9%。
本文整理了本次顶会的入选的精选论文，分享给大家。完整版需要的朋友自取。
ICML2020录取论文完整版源地址：
https://proceedings.icml.cc/book/2020
精选论文分享
Reverse-engineering deep ReLU networks David Rolnick, Konrad Kording
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits Ilai Bistritz, Tavor Baharav, Amir Leshem, Nicholas Bambos
Scalable Differentiable Physics for Learning and Control Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming Lin
Generalization to New Actions in Reinforcement Learning Ayush Jain, Andrew Szot, Joseph Lim
Randomized Block-Diagonal Preconditioning for Parallel Learning Celestine Mendler-Dünner, Aurelien Lucchi
Stochastic Flows and Geometric Optimization on the Orthogonal Group Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, YUAN GAO, Aldo Pacchiano, Tamas Sarlos, Adrian Weller, Vikas Sindhwani
PackIt: A Virtual Environment for Geometric Planning Ankit Goyal, Jia Deng
Soft Threshold Weight Reparameterization for Learnable Sparsity Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, Ali Farhadi
Stochastic Latent Residual Video Prediction Jean-Yves Franceschi, Edouard Delasalles, Mickael Chen, Sylvain Lamprier, Patrick Gallinari
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise Umut Simsekli, Lingjiong Zhu, Yee Whye Teh, Mert Gurbuzbalaban
Context Aware Local Differential Privacy Jayadev Acharya, Keith Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
Privately Learning Markov Random Fields Gautam Kamath, Janardhan Kulkarni, Steven Wu, Huanyu Zhang
A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying
Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke
Enhancing Simple Models by Exploiting What They Already Know Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss
Fiduciary Bandits Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz
Training Deep Energy-Based Models with f-Divergence Minimization Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon
Progressive Graph Learning for Open-Set Domain Adaptation Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh
Learning De-biased Representations with Biased Representations Hyojin Bahng, SANGHYUK CHUN, Sangdoo Yun, Jaegul Choo, Seong Joon Oh
Generalized Neural Policies for Relational MDPs Sankalp Garg, Aniket Bajpai, Mausam
Feature-map-level Online Adversarial Knowledge Distillation Inseop Chung, SeongUk Park, Kim Jangho, NOJUN KWAK
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker
Towards Accurate Post-training Network Quantization via Bit-Split and Stitching Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization Pan Zhou, Xiao-Tong Yuan
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders Alexey Drutsa
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems Tianyi Lin, Chi Jin, Michael Jordan
Training Binary Neural Networks through Learning with Noisy Supervision Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization Geoffrey Negiar, Gideon Dresdner, Alicia Yi-Ting Tsai, Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation Jian Liang, Dapeng Hu, Jiashi Feng
Acceleration through spectral density estimation Fabian Pedregosa, Damien Scieur
Graph Structure of Neural Networks Jiaxuan You, Kaiming He, Jure Leskovec, Saining Xie
Optimal Continual Learning has Perfect Memory and is NP-hard Jeremias Knoblauch, Hisham Husain, Tom Diethe
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens
Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model Ying Jin, Zhaoran Wang, Junwei Lu
On the Number of Linear Regions of Convolutional Neural Networks Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, Ling Shao
Deep Streaming Label Learning Zhen Wang, Liu Liu, Dacheng Tao
From Importance Sampling to Doubly Robust Policy Gradient Jiawei Huang, Nan Jiang
Loss Function Search for Face Recognition Xiaobo Wang, Shuo Wang, Shifeng Zhang, Cheng Chi, Tao Mei
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search Yong Guo, Yaofo Chen, Yin Zheng, Peilin Zhao, Jian Chen, Junzhou Huang, Mingkui Tan
Automatic Reparameterisation of Probabilistic Programs Maria Gorinova, Dave Moore, Matthew Hoffman
Kernel Methods for Cooperative Multi-Agent Learning with Delays Abhimanyu Dubey, Alex `Sandy' Pentlan
dRobust Multi-Agent Decision-Making with Heavy-Tailed Payoffs Abhimanyu Dubey, Alex `Sandy' Pentlan
dLearning the Valuations of a $k$-demand Agent Hanrui Zhang, Vincent Conitzer
Rigging the Lottery: Making All Tickets Winners Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, Erich Elsen
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation Florence Regol, Soumyasundar Pal, Yingxue Zhang, Mark Coates
Performative Prediction Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, University of California Moritz Hardt
On Layer Normalization in the Transformer Architecture Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, Tie-Yan Liu
The many Shapley values for model explanation Mukund Sundararajan, Amir Najmi
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming Daoli Zhu, Lei Zhao
New Oracle-Efficient Algorithms for Private Synthetic Data Release Giuseppe Vietri, Steven Wu, Mark Bun, Thomas Steinke, Grace Tian
Oracle Efficient Private Non-Convex Optimization Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu
Universal Asymptotic Optimality of Polyak Momentum Damien Scieur, Fabian Pedregosa
Adversarial Robustness via Runtime Masking and Cleansing Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung (Brandon) Wu
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability Mingjie Li, Lingshen He, Zhouchen Lin
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting Zixin Zhong, Wang Chi Cheung, Vincent Tan
Robustness to Programmable String Transformations via Augmented Abstract Training Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
The Complexity of Finding Stationary Points with Stochastic Gradient Descent Yoel Drori, Ohad Shamir
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors Zhaoqiang Liu, Selwyn Gomes, Avtansh Tiwari, Jonathan Scarlett
Class-Weighted Classification: Trade-offs and Robust Approaches Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
Neural Architecture Search in a Proxy Validation Loss Landscape Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu
Almost Tune-Free Variance Reduction Bingcong Li, Lingda Wang, Georgios B. Giannakis
Uniform Convergence of Rank-weighted Learning Liu Leqi, Justin Khim, Adarsh Prasad, Pradeep Ravikumar
Non-autoregressive Translation with Disentangled Context Transformer Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu
More Information Supervised Probabilistic Deep Face Embedding Learning Ying Huang, Shangfeng Qiu, Wenwei Zhang, Xianghui Luo, Jinzhuo Wang
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards Aadirupa Saha, Pierre Gaillard, Michal Valko
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model Aadirupa Saha, Aditya Gopalan
Reliable Fidelity and Diversity Metrics for Generative Models Muhammad Ferjad Naeem, Seong Joon Oh, Yunjey Choi, Youngjung Uh, Jaejun Yoo
Learning Factorized Weight Matrix for Joint Image Filtering Xiangyu Xu, Yongrui Ma, Wenxiu Sun
Likelihood-free MCMC with Amortized Approximate Ratio Estimators Joeri Hermans, Volodimir Begy, Gilles Louppe
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli
GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values Shangtong Zhang, Bo Liu, Shimon Whiteson
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik, Martin Vechev
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective Baifeng Shi, Dinghuai Zhang, Qi Dai, Jingdong Wang, Zhanxing Zhu, Yadong Mu
Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters Wenhui Yu, Zheng Qin
SoftSort: A Differantiable Continuous Relaxation of the argsort Operator Sebastian Prillo, Julian Eisenschlos
Too Relaxed to Be Fair Michael Lohaus, Michaël Perrot, Ulrike von Luxburg
Lorentz Group Equivariant Neural Network for Particle Physics Alexander Bogatskiy, Brandon Anderson, Jan Offermann, Marwah Roussi, David Miller, Risi Kondor
One-shot Distributed Ridge Regression in High Dimensions Yue Sheng, Edgar Dobriban
Streaming k-Submodular Maximization under Noise subject to Size Constraint Lan N. Nguyen, My T. Thai
Variational Imitation Learning with Diverse-quality Demonstrations Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
Task Understanding from Confusing Multi-task Data Xin Su, Yizhou Jiang, Shangqi Guo, Feng Chen
Cost-effective Interactive Attention Learning with Neural Attention Process Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang
Channel Equilibrium Networks for Learning Deep Representation Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer Alexey Drutsa
Topological Autoencoders Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
An Accelerated DFO Algorithm for Finite-sum Convex Functions Yuwen Chen, Antonio Orvieto, Aurelien Lucchi
The Shapley Taylor Interaction Index Mukund Sundararajan, Kedar Dhamdhere, Ashish Agarwal
Privately detecting changes in unknown distributions Rachel Cummings, Sara Krehbiel, Yuliia Lut, Wanrong Zhang
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods Wei Zhang, Thomas Panum, Somesh Jha, Prasad Chalasani, David Page
Efficient Continuous Pareto Exploration in Multi-Task Learning Pingchuan Ma, Tao Du, Wojciech Matusik
WaveFlow: A Compact Flow-based Model for Raw Audio Wei Ping, Kainan Peng, Kexin Zhao, Zhao Song
Multi-Agent Determinantal Q-Learning Yaodong Yang, Ying Wen, Jun Wang, Liheng Chen, Kun Shao, David Mguni, Weinan Zhang
Revisiting Spatial Invariance with Low-Rank Local Connectivity Gamaleldin Elsayed, Prajit Ramachandran, Jon Shlens, Simon Kornblith
Minimax Weight and Q-Function Learning for Off-Policy Evaluation Masatoshi Uehara, Jiawei Huang, Nan Jiang
Tensor denoising and completion based on ordinal observations Chanwoo Lee, Miaoyan Wang
Learning Human Objectives by Evaluating Hypothetical Behavior Siddharth Reddy, Anca Dragan, Sergey Levine, Shane Legg, Jan Leike
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models Yuta Saito, Shota Yasui
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time XICHUAN ZHOU, YiCong Peng, Chunqiao Long, Fengbo Ren, Cong Shi
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust Bo Han, Gang Niu, Xingrui Yu, QUANMING YAO, Miao Xu, Ivor Tsang, Masashi Sugiyama
Multinomial Logit Bandit with Low Switching Cost Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John Gregoire, Carla Gomes
Uncertainty-Aware Lookahead Factor Models for Improved Quantitative Investing Lakshay Chauhan, John Alberg, Zachary Lipton
On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness Sebastian Pokutta, Mohit Singh, Alfredo Torrico
Stronger and Faster Wasserstein Adversarial Attacks Kaiwen Wu, Allen Wang, Yaoliang Yu
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer
Why Are Learned Indexes So Effective? Paolo Ferragina, Fabrizio Lillo, Giorgio Vinciguerra
Fast OSCAR and OWL with Safe Screening Rules Runxue Bao, Bin Gu, Heng Huang
Which Tasks Should Be Learned Together in Multi-task Learning? Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization Hien Le, Nicolas Gillis, Panagiotis Patrinos
Adversarial Neural Pruning with Latent Vulnerability Suppression Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Lifted Disjoint Paths with Application in Multiple Object Tracking Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi, Matthias Hein, Philipp Hennig
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning Sai Praneeth Reddy Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Jakkam Reddi, Sebastian Stich, Ananda Theertha Suresh
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization Hadrien Hendrikx, Lin Xiao, Sebastien Bubeck, Francis Bach, Laurent Massoulié
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification Hui Ye, Zhiyu Chen, Da-Han Wang, Brian Davison
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions Ahmed Alaa, Mihaela van der Schaar
Disentangling Trainability and Generalization in Deep Neural Networks Lechao Xiao, Jeffrey Pennington, Samuel Schoenholz
Moniqua: Modulo Quantized Communication in Decentralized SGD Yucheng Lu, Christopher De Sa
Expectation Maximization with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation Amr Mohamed Alexandari, Anshul Kundaje, Avanti Shrikumar
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights and Algorithms Chaosheng Dong, Bo Zeng
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures Mohamed El Amine Seddik, Cosme Louart, Mohamed Tamaazousti, Romain COUILLET
Optimizing Data Usage via Differentiable Rewards Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, Graham Neubig
Optimistic Policy Optimization with Bandit Feedback Lior Shani, Yonathan Efroni, Aviv Rosenberg, Shie Mannor
Maximum-and-Concatenation Networks Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu, Zhouchen Lin
Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition Chi Jin, Tiancheng Jin, Haipeng Luo, Suvrit Sra, Tiancheng Yu
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data Wenkai Xu, Tamara Fernandez, Nicolas Rivera, Arthur Gretton
Efficient Intervention Design for Causal Discovery with Latents Raghavendra Addanki, Shiva Kasiviswanathan, Andrew McGregor, Cameron Musco
Certified Data Removal from Machine Learning Models Chuan Guo, Tom Goldstein, Awni Hannun, Laurens van der Maaten
One Size Fits All: Can We Train One Denoiser for All Noise Levels? Abhiram Gnanasambandam, Stanley Chan
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation Marc Brockschmidt
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir, Nicolas Durrande, James Hensman
Asynchronous Coagent Networks James Kostas, Chris Nota, Philip Thomas
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Taylor Expansion Policy Optimization Yunhao Tang, Michal Valko, Remi Munos
Reinforcement Learning for Integer Programming: Learning to Cut Yunhao Tang, Shipra Agrawal, Yuri Faenza
Safe Reinforcement Learning in Constrained Markov Decision Processes Akifumi Wachi, Yanan Sui
Layered Sampling for Robust Optimization Problems Hu Ding, Zixiu Wang
Learning to Encode Position for Transformer with Continuous Dynamical Model Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-Jui Hsieh
Do RNN and LSTM have Long Memory? Jingyu Zhao, Feiqing Huang, Jia Lv, Yanjie Duan, Zhen Qin, Guodong Li, Guangjian Tian
Training Linear Neural Networks: Non-Local Convergence and Complexity Results Armin Eftekhari
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies Hengrui Cai, Wenbin Lu, Rui Song
Graph Optimal Transport for Cross-Domain Alignment Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu
Approximation Capabilities of Neural ODEs and Invertible Residual Networks Han Zhang, Xi Gao, Jacob Unterman, Tomasz Arodz
Refined bounds for algorithm configuration: The knife-edge of dual class approximability Nina Balcan, Tuomas Sandholm, Ellen Vitercik
Teaching with Limited Information on the Learner's Behaviour Ferdinando Cicalese, Francisco Sergio de Freitas Filho, Eduardo Laber, Marco Molinaro
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge Laura Rieger, Chandan Singh, William Murdoch, Bin Yu
DeltaGrad: Rapid retraining of machine learning models Yinjun Wu, Edgar Dobriban, Susan Davidson
The Cost-free Nature of Optimally Tuning Tikhonov Regularizers and Other Ordered Smoothers Pierre Bellec, Dana Yang
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions Kaito Fujii
Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent Yunwen Lei, Yiming Ying
Online Dense Subgraph Discovery via Blurred-Graph Feedback Yuko Kuroki, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash
Perceptual Generative Autoencoders Zijun Zhang, Ruixiang ZHANG, Zongpeng Li, Yoshua Bengio, Liam Paull
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks Yuxin Wen, Shuai Li, Kui Jia
Stochastic Gradient and Langevin Processes Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles Tonghan Wang, Heng Dong, Victor Lesser, Chongjie Zhang
Minimax Pareto Fairness: A Multi Objective Perspective Martin Bertran, Natalia Martinez, Guillermo Sapiro
Online Pricing with Offline Data: Phase Transition and Inverse Square Law Jinzhi Bu, David Simchi-Levi, Yunzong Xu
Explicit Gradient Learning for Black-Box Optimization Elad Sarafian, Mor Sinay, yoram louzoun, Noa Agmon, Sarit Kraus
Optimization and Analysis of the pAp@k Metric for Recommender Systems Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt, Maximilian Granz, Tim Landgraf
Naive Exploration is Optimal for Online LQR Max Simchowitz, Dylan Foster
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective Ruixiang ZHANG, Katsuhiko Ishiguro, Masanori Koyama
Implicit Generative Modeling for Efficient Exploration Neale Ratzlaff, Qinxun Bai, Fuxin Li, Wei Xu
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, Wojciech Matusik
Goodness-of-Fit Tests for Inhomogeneous Random Graphs Soham Dan, Bhaswar B. Bhattacharya
Few-shot Domain Adaptation by Causal Mechanism Transfer Takeshi Teshima, Issei Sato, Masashi Sugiyama
Adaptive Adversarial Multi-task Representation Learning YUREN MAO, Weiwei Liu, Xuemin Lin
Streaming Submodular Maximization under a k-Set System Constraint Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi
A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
Optimal approximation for unconstrained non-submodular minimization Marwa El Halabi, Stefanie Jegelka
Generating Programmatic Referring Expressions via Program Synthesis Jiani Huang, Calvin Smith, Osbert Bastani, Rishabh Singh, Aws Albarghouthi, Mayur Naik
Nearly Linear Row Sampling Algorithm for Quantile Regression Yi Li, Ruosong Wang, Lin Yang, Hanrui Zhang
On Leveraging Pretrained GANs for Generation with Limited Data Miaoyun Zhao, Yulai Cong, Lawrence Carin
More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models Lin Chen, Yifei Min, Mingrui Zhang, Amin Karbasi
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation Nathan Kallus, Masatoshi Uehara
Statistically Efficient Off-Policy Policy Gradients Nathan Kallus, Masatoshi Uehara
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training Xuxi Chen, Wuyang Chen, Tianlong Chen, Ye Yuan, Chen Gong, Kewei Chen, Zhangyang Wang
When Does Self-Supervision Help Graph Convolutional Networks? Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu
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
Ready Policy One: World Building Through Active Learning Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Structural Language Models of Code Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter Liu
Aggregation of Multiple Knockoffs Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Thirion Bertrand, Sylvain Arlot
Off-Policy Actor-Critic with Shared Experience Replay Simon Schmitt, Matteo Hessel, Karen Simonyan
Graph-based Nearest Neighbor Search: From Practice to Theory Liudmila Prokhorenkova, Aleksandr Shekhovtsov
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning Amin Rakhsha, Goran Radanovic, Rati Devidze, Jerry Zhu, Adish Singla
Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball Dejun Chu, Changshui Zhang, Shiliang Sun, Qing Tao
Influenza Forecasting Framework based on Gaussian Processes Christoph Zimmer, Reza Yaesoubi
Unique Properties of Wide Minima in Deep Networks Rotem Mulayoff, Tomer Michaeli
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making Chengchun Shi, Runzhe Wan, Rui Song, Wenbin Lu, Ling Leng
LTF: A Label Transformation Framework for Correcting Label Shift Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d'Alche-Buc
Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health Liangyu Zhu, Wenbin Lu, Rui Song
Towards Understanding the Dynamics of the First-Order Adversaries Zhun Deng, Hangfeng He, Jiaoyang Huang, Weijie Su
Interpreting Robust Optimization via Adversarial Influence Functions Zhun Deng, Cynthia Dwork, Jialiang Wang, Linjun Zhang
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations Markos Georgopoulos, Grigorios Chrysos, Yannis Panagakis, Maja Pantic
No-Regret Exploration in Goal-Oriented Reinforcement Learning Jean Tarbouriech, Evrard Garcelon, Michal Valko, Matteo Pirotta, Alessandro Lazaric
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, Joel Z Leibo
Feature Noise Induces Loss Discrepancy Across Groups Fereshte Khani, Percy Liang
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics Gregor Simm, Robert Pinsler, Jose Miguel Hernandez-Lobato
Small-GAN: Speeding up GAN Training using Core-Sets Samrath Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena
Conditional gradient methods for stochastically constrained convex minimization Maria-Luiza Vladarean, Ahmet Alacaoglu, Ya-Ping Hsieh, Volkan Cevher
Undirected Graphical Models as Approximate Posteriors Arash Vahdat, Evgeny Andriyash, William Macready
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy Jiaoyang Huang, Horng-Tzer Yau
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics Debjani Saha, Candice Schumann, Duncan McElfresh, John Dickerson, Michelle Mazurek, Michael Tschantz
Encoding Musical Style with Transformer Autoencoders Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel
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, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly
ConQUR: Mitigating Delusional Bias in Deep Q-Learning DiJia Su, Jayden Ooi, Tyler Lu, Dale Schuurmans, Craig Boutilier
Self-Modulating Nonparametric Event-Tensor Factorization Zheng Wang, Xinqi Chu, Shandian Zhe
Extreme Multi-label Classification from Aggregated Labels Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit Dhillon
Full Law Identification In Graphical Models Of Missing Data: Completeness Results Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Self-Attentive Associative Memory Hung Le, Truyen Tran, Svetha Venkatesh
Imputer: Sequence Modelling via Imputation and Dynamic Programming William Chan, Chitwan Saharia, Geoffrey Hinton, Mohammad Norouzi, Navdeep Jaitly
Continuously Indexed Domain Adaptation Hao Wang, Hao He, Dina Katabi
Evolving Machine Learning Algorithms From Scratch Esteban Real, Chen Liang, David So, Quoc Le
Self-Attentive Hawkes Process Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz
On hyperparameter tuning in general clustering problemsm Xinjie Fan, Yuguang Yue, Purnamrita Sarkar, Y. X. Rachel Wang
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Adaptive Region-Based Active Learning Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
Robust Outlier Arm Identification Yinglun Zhu, Sumeet Katariya, Robert Nowak
Provably Efficient Exploration in Policy Optimization Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
Striving for simplicity and performance in off-policy DRL: Output Normalization and Non-Uniform Sampling Che Wang, Yanqiu Wu, Quan Vuong, Keith Ross
Multidimensional Shape Constraints Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Tamann Narayan, Sen Zhao
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima
Operation-Aware Soft Channel Pruning using Differentiable Masks Minsoo Kang, Bohyung Han
Normalized Loss Functions for Deep Learning with Noisy Labels Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, James Bailey
Learning Deep Kernels for Non-Parametric Two-Sample Tests Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, D.J. Sutherlan
dDeBayes: a Bayesian method for debiasing network embeddings Maarten Buyl, Tijl De Bie
Principled learning method for Wasserstein distributionally robust optimization with local perturbations Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik
Low-Variance and Zero-Variance Baselines for Extensive-Form Games Trevor Davis, Martin Schmid, Michael Bowling
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games Youzhi Zhang, Bo An
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks Alexander Shevchenko, Marco Mondelli
Leveraging Frequency Analysis for Deep Fake Image Recognition Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Dorothea Kolossa, Thorsten Holz, Asja Fischer
Tails of Lipschitz Triangular Flows Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus Brubaker
Deep Coordination Graphs Wendelin Boehmer, Vitaly Kurin, Shimon Whiteson
Voice Separation with an Unknown Number of Multiple Speakers Eliya Nachmani, Yossi Adi, Lior Wolf
Predicting Choice with Set-Dependent Aggregation Nir Rosenfeld, Kojin Oshiba, Yaron Singer
Thompson Sampling Algorithms for Mean-Variance Bandits Qiuyu Zhu, Vincent Tan
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Schober, Philipp Hennig
Debiased Sinkhorn barycenters Hicham Janati, Marco Cuturi, Alexandre Gramfort
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills Victor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-i-Nieto, Jordi Torres
Sparsified Linear Programming for Zero-Sum Equilibrium Finding Brian Zhang, Tuomas Sandholm
Extra-gradient with player sampling for faster convergence in n-player games Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna
Entropy Minimization In Emergent Languages Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco Baroni
Spectral Clustering with Graph Neural Networks for Graph Pooling Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi
VFlow: More Expressive Generative Flows with Variational Data Augmentation Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
Fully Parallel Hyperparameter Search: Reshaped Space-Filling Marie-Liesse Cauwet, Camille Couprie, Julien Dehos, Pauline Luc, Jeremy Rapin, Morgane Riviere, Fabien Teytaud, Olivier Teytaud, Nicolas Usunier
Discount Factor as a Regularizer in Reinforcement Learning Ron Amit, Kamil Ciosek, Ron Meir
On Learning Sets of Symmetric Elements Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
Learning Similarity Metrics for Numerical Simulations Georg Kohl, Kiwon Um, Nils Thuerey
FR-Train: A mutual information-based approach to fair and robust training Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
Real-Time Optimisation for Online Learning in Auctions Lorenzo Croissant, Marc Abeille, Clément Calauzènes
Graph Random Neural Features for Distance-Preserving Graph Representations Daniele Zambon, Cesare Alippi, Lorenzo Livi
Modulating Surrogates for Bayesian Optimization Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill Campbell, Carl Henrik Ek
Convolutional Kernel Networks for Graph-Structured Data Dexiong Chen, Laurent Jacob, Julien Mairal
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking Haoran Sun, Songtao Lu, Mingyi Hong
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features Liang Ding, Rui Tuo, Shahin Shahrampour
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle Shaocong Ma, Yi Zhou
Learning Opinions in Social Networks Vincent Conitzer, Debmalya Panigrahi, Hanrui Zhang
Latent Variable Modelling with Hyperbolic Normalizing Flows Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, Will Hamilton
StochasticRank: Global Optimization of Scale-Free Discrete Functions Aleksei Ustimenko, Liudmila Prokhorenkova
Working Memory Graphs Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio
Spread Divergence Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
Optimizing Black-box Metrics with Adaptive Surrogates Qijia Jiang, Olaoluwa Adigun, Harikrishna Narasimhan, Mahdi Milani Fard, Maya Gupta
Domain Adaptive Imitation Learning Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
A general recurrent state space framework for modeling neural dynamics during decision-making David Zoltowski, Jonathan Pillow, Scott Linderman
An Imitation Learning Approach for Cache Replacement Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning Karsten Roth, Timo Milbich, Samrath Sinha, Prateek Gupta, Bjorn Ommer, Joseph Paul Cohen
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression Changhee Lee, Mihaela van der Schaar
Countering Language Drift with Seeded Iterated Learning Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization Quoc Tran-Dinh, Nhan Pham, Lam Nguyen
Strategyproof Mean Estimation from Multiple-Choice Questions Anson Kahng, Gregory Kehne, Ariel Procaccia
Sequential Cooperative Bayesian Inference Junqi Wang, Pei Wang, Patrick Shafto
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory Zhou Fan, Cheng Mao, Yihong Wu, Jiaming Xu
Zeno++: Robust Fully Asynchronous SGD Cong Xie, Sanmi Koyejo, Indranil Gupta
Network Pruning by Greedy Subnetwork Selection Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently Asaf Cassel, Alon Cohen, Tomer Koren
Hierarchical Verification for Adversarial Robustness Cong Han Lim, Raquel Urtasun, Ersin Yumer
BINOCULARS for efficient, nonmyopic sequential experimental design Shali Jiang, Henry Chai, Javier Gonzalez, Roman Garnett
On the Global Optimality of Model-Agnostic Meta-Learning Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
Learning with Bounded Instance- and Label-dependent Label Noise Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, Dacheng Tao
Transparency Promotion with Model-Agnostic Linear Competitors Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani
Learning Mixtures of Graphs from Epidemic Cascades Jessica Hoffmann, Soumya Basu, Surbhi Goel, Constantine Caramanis
Implicit differentiation of Lasso-type models for hyperparameter optimization Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, Joseph Salmon
Latent Space Factorisation and Manipulation via Matrix Subspace Projection Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin
Active World Model Learning in Agent-rich Environments with Progress Curiosity Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates Lingkai Kong, Jimeng Sun, Chao Zhang
GANs May Have No Nash Equilibria Farzan Farnia, Asuman Ozdaglar
Gradient Temporal-Difference Learning with Regularized Corrections Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gutpa, Adam White, Martha White
Online mirror descent and dual averaging: keeping pace in the dynamic case Huang Fang, Victor Sanches Portella, Nick Harvey, Michael Friedlander
Choice Set Optimization Under Discrete Choice Models of Group Decisions Kiran Tomlinson, Austin Benson
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Suvrit Sra, Ali Jadbabaie
Multi-Agent Routing Value Iteration Network Quinlan Sykora, Mengye Ren, Raquel Urtasun
Adversarial Attacks on Copyright Detection Systems Parsa Saadatpanah, Ali Shafahi, Tom Goldstein
Differentiating through the Fréchet Mean Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser Nam Lim, Christopher De Sa
Online Learning for Active Cache Synchronization Andrey Kolobov, Sebastien Bubeck, Julian Zimmert
PoKED: A Semi-Supervised System for Word Sense Disambiguation Feng Wei
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation Pan Xu, Quanquan Gu
Understanding and Stabilizing GANs' Training Dynamics Using Control Theory Kun Xu, Chongxuan Li, Jun Zhu, Bo Zhang
Scalable Nearest Neighbor Search for Optimal Transport Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner
Supervised learning: no loss no cry Richard Nock, Aditya Menon
Label-Noise Robust Domain Adaptation Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao
Description Based Text Classification with Reinforcement Learning Wei Wu, Duo Chai, Qinghong Han, Fei Wu, Jiwei Li
Bandits for BMO Functions Tianyu Wang, Cynthia Rudin
Cost-effectively Identifying Causal Effect When Only Response Variable Observable Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, Zhi-Hua Zhou
Learning with Multiple Complementary Labels LEI FENG, Takuo Kaneko, Bo Han, Gang Niu, Bo An, Masashi Sugiyama
Contrastive Multi-View Representation Learning on Graphs Kaveh Hassani, Amir Hosein Khasahmadi
A Chance-Constrained Generative Framework for Sequence Optimization Xianggen Liu, Jian Peng, Qiang Liu, Sen Song
dS^2LBI: Exploring Structural Sparsity on Deep Network via Differential Inclusion Paths Yanwei Fu, Chen Liu, Donghao Li, Xinwei Sun, Jinshan ZENG, Yuan Yao
Sparse Subspace Clustering with Entropy-Norm Liang Bai, Jiye Liang
On the Generalization Effects of Linear Transformations in Data Augmentation Sen Wu, Hongyang Zhang, Gregory Valiant, Christopher Re
Sparse Shrunk Additive Models Hong Chen, guodong liu, Heng Huang
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space Andrey Voynov, Artem Babenko
DropNet: Reducing Neural Network Complexity via Iterative Pruning Chong Min John Tan, Mehul Motani
Self-supervised Label Augmentation via Input Transformations Hankook Lee, Sung Ju Hwang, Jinwoo Shin
Mapping natural-language problems to formal-language solutions using structured neural representations Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Ken Forbus, Jianfeng Gao
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time Zahra Monfared, Daniel Durstewitz
Implicit Geometric Regularization for Learning Shapes Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman
Influence Diagram Bandits Tong Yu, Branislav Kveton, Zheng Wen, Ruiyi Zhang, Ole J. Mengshoel
Information Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains Johannes Fischer, Ömer Sahin Tas
Convergence Rates of Variational Inference in Sparse Deep Learning Badr-Eddine Chérief-Abdellatif
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks Zhiyu Yao, Yunbo Wang, Mingsheng Long, Jianmin Wang
DINO: Distributed Newton-Type Optimization Method Rixon Crane, Fred Roosta
Quantum Expectation-Maximization for Gaussian Mixture Models Alessandro Luongo, Iordanis Kerenidis, Anupam Prakash
Consistent Structured Prediction with Max-Min Margin Markov Networks Alex Nowak, Francis Bach, Alessandro Rudi
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions Prashanth L.A., Krishna Jagannathan, Ravi Kolla
Robust Pricing in Dynamic Mechanism Design Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
Nested Subspace Arrangement for Representation of Relational Data Nozomi Hata, Shizuo Kaji, Akihiro Yoshida, Katsuki Fujisawa
Equivariant Neural Rendering Emilien Dupont, Miguel Bautista Martin, Alex Colburn, Aditya Sankar, Joshua Susskind, Qi Shan
Bounding the fairness and accuracy of classifiers from population statistics Sivan Sabato, Elad Yom-Tov
Healing Gaussian Process Experts samuel cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan Foster, Alexander Rakhlin
Simple and Deep Graph Convolutional Networks Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li
Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
Meta Variance Transfer: Learning to Augment from the Others Seong-Jin Park, Seungju Han, Ji-won Baek, Insoo Kim, Juhwan Song, Hae Beom Lee, Jae-Joon Han, Sung Ju Hwang
Coresets for Clustering in Graphs of Bounded Treewidth Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
On Breaking Deep Generative Model-based Defenses and Beyond Yanzhi Chen, Renjie Xie, Zhanxing Zhu
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar
Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer Anton Zhiyanov, Alexey Drutsa