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机器学习领域顶会ICML20精选论文分享

深度学习与NLP • 3 年前 • 251 次点击  


    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



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