General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

78 Results

Oral
Wed 5:00 Near Optimal Reward-Free Reinforcement Learning
Zhang Zihan, Simon Du, Xiangyang Ji
Spotlight
Wed 5:20 Batch Value-function Approximation with Only Realizability
Tengyang Xie, Nan Jiang
Spotlight
Wed 5:35 Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, Mengdi Wang
Oral
Wed 6:00 Dynamic Game Theoretic Neural Optimizer
Guan-Horng Liu, CHEN Chen, Evangelos Theodorou
Oral
Wed 6:00 Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Spotlight
Wed 6:20 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
Yaqi Duan, Chi Jin, Zhiyuan Li
Spotlight
Wed 6:25 Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Asaf Cassel, Tomer Koren
Spotlight
Wed 6:30 Reward Identification in Inverse Reinforcement Learning
Kuno Kim, Shivam Garg, Kiran Shiragur, Stefano Ermon
Spotlight
Wed 6:35 Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Yun Kuen Cheung, Georgios Piliouras
Spotlight
Wed 6:40 Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
Spotlight
Wed 6:40 On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
Tianhao Wu, Yunchang Yang, Simon Du, Liwei Wang
Spotlight
Wed 6:45 Kernel-Based Reinforcement Learning: A Finite-Time Analysis
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Spotlight
Wed 6:45 Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
Kishan Panaganti, Dileep Kalathil
Poster
Wed 9:00 Near Optimal Reward-Free Reinforcement Learning
Zhang Zihan, Simon Du, Xiangyang Ji
Poster
Wed 9:00 Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
Kishan Panaganti, Dileep Kalathil
Poster
Wed 9:00 On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP
Tianhao Wu, Yunchang Yang, Simon Du, Liwei Wang
Poster
Wed 9:00 Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence
Yun Kuen Cheung, Georgios Piliouras
Poster
Wed 9:00 Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
Poster
Wed 9:00 Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Asaf Cassel, Tomer Koren
Poster
Wed 9:00 Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvari, Mengdi Wang
Poster
Wed 9:00 Batch Value-function Approximation with Only Realizability
Tengyang Xie, Nan Jiang
Poster
Wed 9:00 Kernel-Based Reinforcement Learning: A Finite-Time Analysis
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Poster
Wed 9:00 Reward Identification in Inverse Reinforcement Learning
Kuno Kim, Shivam Garg, Kiran Shiragur, Stefano Ermon
Poster
Wed 9:00 Dynamic Game Theoretic Neural Optimizer
Guan-Horng Liu, CHEN Chen, Evangelos Theodorou
Poster
Wed 9:00 Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon Du, Sham Kakade, Jason Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
Poster
Wed 9:00 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
Yaqi Duan, Chi Jin, Zhiyuan Li
Oral
Wed 17:00 Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Andrea Zanette
Oral
Wed 17:00 UCB Momentum Q-learning: Correcting the bias without forgetting
Pierre MENARD, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
Spotlight
Wed 17:25 Confidence-Budget Matching for Sequential Budgeted Learning
Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor
Spotlight
Wed 17:30 Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
Spotlight
Wed 17:30 Fast active learning for pure exploration in reinforcement learning
Pierre MENARD, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
Spotlight
Wed 17:35 Leveraging Non-uniformity in First-order Non-convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Spotlight
Wed 17:40 Robust Policy Gradient against Strong Data Corruption
Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun
Spotlight
Wed 17:45 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
Oral
Wed 18:00 Task-Optimal Exploration in Linear Dynamical Systems
Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
Oral
Wed 18:00 Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
Spotlight
Wed 18:20 Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra
Spotlight
Wed 18:25 CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
Tengyu Xu, Yingbin LIANG, Guanghui Lan
Spotlight
Wed 18:25 A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
Spotlight
Wed 18:30 Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin LIANG
Spotlight
Wed 18:30 Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang
Spotlight
Wed 18:35 Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
Yujia Jin, Aaron Sidford
Spotlight
Wed 18:40 Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case
Liyu Chen, Haipeng Luo
Oral
Wed 19:00 Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari
Spotlight
Wed 19:20 Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs
Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar
Spotlight
Wed 19:30 Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang
Spotlight
Wed 19:35 Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan Wagener, Byron Boots, Ching-An Cheng
Spotlight
Wed 19:45 Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Gokul Swamy, Sanjiban Choudhury, J. Bagnell, Steven Wu
Poster
Wed 21:00 Confidence-Budget Matching for Sequential Budgeted Learning
Yonathan Efroni, Nadav Merlis, Aadirupa Saha, Shie Mannor
Poster
Wed 21:00 Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs
Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, Tamer Basar
Poster
Wed 21:00 Fast active learning for pure exploration in reinforcement learning
Pierre MENARD, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
Poster
Wed 21:00 UCB Momentum Q-learning: Correcting the bias without forgetting
Pierre MENARD, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
Poster
Wed 21:00 Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality
Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin LIANG
Poster
Wed 21:00 Task-Optimal Exploration in Linear Dynamical Systems
Andrew Wagenmaker, Max Simchowitz, Kevin Jamieson
Poster
Wed 21:00 A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin
Poster
Wed 21:00 CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee
Tengyu Xu, Yingbin LIANG, Guanghui Lan
Poster
Wed 21:00 Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL
Andrea Zanette
Poster
Wed 21:00 Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
Poster
Wed 21:00 Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvari
Poster
Wed 21:00 Provably Correct Optimization and Exploration with Non-linear Policies
Fei Feng, Wotao Yin, Alekh Agarwal, Lin Yang
Poster
Wed 21:00 Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
Poster
Wed 21:00 Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu, Suvrit Sra
Poster
Wed 21:00 Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Gokul Swamy, Sanjiban Choudhury, J. Bagnell, Steven Wu
Poster
Wed 21:00 Leveraging Non-uniformity in First-order Non-convex Optimization
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvari, Dale Schuurmans
Poster
Wed 21:00 Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
Yujia Jin, Aaron Sidford
Poster
Wed 21:00 Randomized Exploration in Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang
Poster
Wed 21:00 Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan Wagener, Byron Boots, Ching-An Cheng
Poster
Wed 21:00 Robust Policy Gradient against Strong Data Corruption
Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun
Poster
Wed 21:00 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou, Quanquan Gu
Poster
Wed 21:00 Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case
Liyu Chen, Haipeng Luo
Oral
Thu 6:00 Temporal Difference Learning as Gradient Splitting
Rui Liu, Alex Olshevsky
Spotlight
Thu 6:20 First-Order Methods for Wasserstein Distributionally Robust MDP
Julien Grand-Clement, Christian Kroer
Spotlight
Thu 6:30 Adaptive Sampling for Best Policy Identification in Markov Decision Processes
Aymen Al Marjani, Alexandre Proutiere
Spotlight
Thu 6:35 Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler
Poster
Thu 9:00 Quantum algorithms for reinforcement learning with a generative model
Daochen Wang, Aarthi Sundaram, Robin Kothari, Ashish Kapoor, Martin Roetteler
Poster
Thu 9:00 First-Order Methods for Wasserstein Distributionally Robust MDP
Julien Grand-Clement, Christian Kroer
Poster
Thu 9:00 Adaptive Sampling for Best Policy Identification in Markov Decision Processes
Aymen Al Marjani, Alexandre Proutiere
Poster
Thu 9:00 Temporal Difference Learning as Gradient Splitting
Rui Liu, Alex Olshevsky