Csaba Szepesvári

ORCID

Publication Activity (10 Years)

Years Active: 1993-2023
Publications (10 Years): 242

Top Venues

Publications

Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans
The Role of Baselines in Policy Gradient Optimization. CoRR (2023)
Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvári, Zhaoran Wang
Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics. ICLR (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. CoRR (2023)
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. AISTATS (2023)
Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvári, Dale Schuurmans
Ordering-based Conditions for Global Convergence of Policy Gradient Methods. NeurIPS (2023)
Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR (2023)
David Janz, Alexander E. Litvak, Csaba Szepesvári
Ensemble sampling for linear bandits: small ensembles suffice. CoRR (2023)
Dong Yin, Sridhar Thiagarajan, Nevena Lazic, Nived Rajaraman, Botao Hao, Csaba Szepesvári
Sample Efficient Deep Reinforcement Learning via Local Planning. CoRR (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Gellért Weisz, András György, Csaba Szepesvári
-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. CoRR (2023)
Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans
Stochastic Gradient Succeeds for Bandits. ICML (2023)
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. CoRR (2023)
Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun
Revisiting Simple Regret: Fast Rates for Returning a Good Arm. ICML (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk, Csaba Szepesvári
Regret Minimization via Saddle Point Optimization. NeurIPS (2023)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. CoRR (2023)
Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR (2023)
Gellért Weisz, András György, Csaba Szepesvári
-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. NeurIPS (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. NeurIPS (2023)
Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT (2023)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. ICML (2023)
David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
Exploration via linearly perturbed loss minimisation. CoRR (2023)
Chung-Wei Lee, Qinghua Liu, Yasin Abbasi-Yadkori, Chi Jin, Tor Lattimore, Csaba Szepesvári
Context-lumpable stochastic bandits. NeurIPS (2023)
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. STOC (2023)
Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. CoRR (2022)
Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári
The Curse of Passive Data Collection in Batch Reinforcement Learning. AISTATS (2022)
Sharan Vaswani, Lin F. Yang, Csaba Szepesvári
Near-Optimal Sample Complexity Bounds for Constrained MDPs. CoRR (2022)
Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup
Towards Painless Policy Optimization for Constrained MDPs. CoRR (2022)
Gellért Weisz, Csaba Szepesvári, András György
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. ALT (2022)
Jincheng Mei, Wesley Chung, Valentin Thomas, Bo Dai, Csaba Szepesvári, Dale Schuurmans
The Role of Baselines in Policy Gradient Optimization. NeurIPS (2022)
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR (2022)
Anant Raj, Pooria Joulani, András György, Csaba Szepesvári
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. AISTATS (2022)
Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
-realizable MDPs. CoRR (2022)
Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
When Is Partially Observable Reinforcement Learning Not Scary? COLT (2022)
Ilja Kuzborskij, Csaba Szepesvári
Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks. CoRR (2022)
Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. NeurIPS (2022)
Sharan Vaswani, Lin Yang, Csaba Szepesvári
Near-Optimal Sample Complexity Bounds for Constrained MDPs. NeurIPS (2022)
Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin
When Is Partially Observable Reinforcement Learning Not Scary? CoRR (2022)
Qinghua Liu, Csaba Szepesvári, Chi Jin
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. NeurIPS (2022)
Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun
Revisiting Simple Regret Minimization in Multi-Armed Bandits. CoRR (2022)
Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári
Confident Least Square Value Iteration with Local Access to a Simulator. AISTATS (2022)
Qinghua Liu, Csaba Szepesvári, Chi Jin
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games. CoRR (2022)
Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári, Bo Dai
A free lunch from the noise: Provable and practical exploration for representation learning. UAI (2022)
Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. NeurIPS (2022)
Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári
Efficient local planning with linear function approximation. ALT (2022)
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári
Adaptive Approximate Policy Iteration. AISTATS (2021)
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári
Improved Regret Bound and Experience Replay in Regularized Policy Iteration. ICML (2021)
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen Triki, Razvan Pascanu
On the Role of Optimization in Double Descent: A Least Squares Study. CoRR (2021)
Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. NeurIPS (2021)
Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári, Bo Dai
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning. CoRR (2021)
Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans
On the Optimality of Batch Policy Optimization Algorithms. ICML (2021)
Ilja Kuzborskij, Csaba Szepesvári
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping. CoRR (2021)
Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári
No Regrets for Learning the Prior in Bandits. CoRR (2021)
Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT (2021)
Gellért Weisz, Philip Amortila, Csaba Szepesvári
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. ALT (2021)
María Pérez-Ortiz, Omar Risvaplata, John Shawe-Taylor, Csaba Szepesvári
Tighter Risk Certificates for Neural Networks. J. Mach. Learn. Res. 22 (2021)
Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. COLT (2021)
Nevena Lazic, Botao Hao, Yasin Abbasi-Yadkori, Dale Schuurmans, Csaba Szepesvári
Optimization Issues in KL-Constrained Approximate Policy Iteration. CoRR (2021)
Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans
Understanding the Effect of Stochasticity in Policy Optimization. CoRR (2021)
Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári
No Regrets for Learning the Prior in Bandits. NeurIPS (2021)
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári
Meta-Thompson Sampling. CoRR (2021)
Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. AISTATS (2021)
Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. CoRR (2021)
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans
Leveraging Non-uniformity in First-order Non-convex Optimization. CoRR (2021)
Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Online Sparse Reinforcement Learning. AISTATS (2021)
Han Zhong, Zhuoran Yang, Zhaoran Wang, Csaba Szepesvári
Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs. CoRR (2021)
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang
Bootstrapping Statistical Inference for Off-Policy Evaluation. CoRR (2021)
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvári, Raia Hadsell, Nicolas Heess, Martin A. Riedmiller
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR (2021)
Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári
Efficient Local Planning with Linear Function Approximation. CoRR (2021)
Ilja Kuzborskij, Csaba Szepesvári
Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping. COLT (2021)
Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. ICML (2021)
Jincheng Mei, Yue Gao, Bo Dai, Csaba Szepesvári, Dale Schuurmans
Leveraging Non-uniformity in First-order Non-convex Optimization. ICML (2021)
Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. CoRR (2021)
Yuxi Li, Alborz Geramifard, Lihong Li, Csaba Szepesvári, Tao Wang
Guest editorial: special issue on reinforcement learning for real life. Mach. Learn. 110 (9) (2021)
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvári
A Distribution-dependent Analysis of Meta Learning. ICML (2021)
Chenjun Xiao, Ilbin Lee, Bo Dai, Dale Schuurmans, Csaba Szepesvári
On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data. CoRR (2021)
Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans
On the Optimality of Batch Policy Optimization Algorithms. CoRR (2021)
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori, Csaba Szepesvári
Improved Regret Bound and Experience Replay in Regularized Policy Iteration. CoRR (2021)
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu
On the Role of Optimization in Double Descent: A Least Squares Study. NeurIPS (2021)
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
Asymptotically Optimal Information-Directed Sampling. COLT (2021)
Jincheng Mei, Bo Dai, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans
Understanding the Effect of Stochasticity in Policy Optimization. NeurIPS (2021)
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári
Meta-Thompson Sampling. ICML (2021)
Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference. ICML (2021)
Gellért Weisz, Csaba Szepesvári, András György
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. CoRR (2021)
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
Asymptotically Optimal Information-Directed Sampling. CoRR (2020)
Roshan Shariff, Csaba Szepesvári
Efficient Planning in Large MDPs with Weak Linear Function Approximation. NeurIPS (2020)
Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans
On the Global Convergence Rates of Softmax Policy Gradient Methods. CoRR (2020)
Zeyu Jia, Lin Yang, Csaba Szepesvári, Mengdi Wang
Model-Based Reinforcement Learning with Value-Targeted Regression. L4DC (2020)
Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans
On the Global Convergence Rates of Softmax Policy Gradient Methods. ICML (2020)
Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. NeurIPS (2020)
Tor Lattimore, Csaba Szepesvári
Exploration by Optimisation in Partial Monitoring. COLT (2020)
Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. CoRR (2020)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. ICLR (2020)
Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin F. Yang
Model-Based Reinforcement Learning with Value-Targeted Regression. CoRR (2020)
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
Model Selection in Contextual Stochastic Bandit Problems. CoRR (2020)
Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. NeurIPS (2020)
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer
Differentiable Meta-Learning of Bandit Policies. NeurIPS (2020)
Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans
CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Csaba Szepesvári