LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. - Gellért Weisz; András György 001; Csaba Szepesvári (ICML, 2018)

Sample Efficient Deep Reinforcement Learning for Dialogue Systems With Large Action Spaces. - Gellért Weisz; Pawel Budzianowski; Pei-Hao Su; Milica Gasic (IEEE ACM Trans. Audio Speech Lang. Process., 2018)

CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. - Gellért Weisz; András György 001; Csaba Szepesvári (ICML, 2019)

Exploration-Enhanced POLITEX. - Yasin Abbasi-Yadkori; Nevena Lazic; Csaba Szepesvári; Gellért Weisz (CoRR, 2019)

Learning with Good Feature Representations in Bandits and in RL with a Generative Model. - Tor Lattimore; Csaba Szepesvári; Gellért Weisz (ICML, 2020)

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. - Gellért Weisz; András György 001; Wei-I Lin; Devon R. Graham; Kevin Leyton-Brown; Csaba Szepesvári; Brendan Lucier (NeurIPS, 2020)

Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. - Gellért Weisz; Philip Amortila; Csaba Szepesvári (ALT, 2021)

On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. - Gellért Weisz; Philip Amortila; Barnabás Janzer; Yasin Abbasi-Yadkori; Nan Jiang 008; Csaba Szepesvári (COLT, 2021)

TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. - Gellért Weisz; Csaba Szepesvári; András György 001 (ALT, 2022)

Confident Approximate Policy Iteration for Efficient Local Planning in $q^ pi$-realizable MDPs. - Gellért Weisz; András György 001; Tadashi Kozuno; Csaba Szepesvári (NeurIPS, 2022)

Confident Approximate Policy Iteration for Efficient Local Planning in qπ-realizable MDPs. - Gellért Weisz; András György 001; Tadashi Kozuno; Csaba Szepesvári (CoRR, 2022)

Exponential Hardness of Reinforcement Learning with Linear Function Approximation. - Sihan Liu; Gaurav Mahajan; Daniel Kane 001; Shachar Lovett; Gellért Weisz; Csaba Szepesvári (COLT, 2023)

Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. - Qinghua Liu; Gellért Weisz; András György 001; Chi Jin 001; Csaba Szepesvári (NeurIPS, 2023)

Online RL in Linearly qπ-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. - Gellért Weisz; András György 001; Csaba Szepesvári (NeurIPS, 2023)

Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear qπ-Realizability and Concentrability. - Volodymyr Tkachuk; Gellért Weisz; Csaba Szepesvári (NeurIPS, 2024)