机器学习领域顶会ICML20精选论文分享

    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