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ORCIDGellért Weisz
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2020 – today
- 2024
[i11]Volodymyr Tkachuk, Gellért Weisz, Csaba Szepesvári:
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear qπ-Realizability and Concentrability. CoRR abs/2405.16809 (2024)- 2023
- [c12]Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT 2023: 1588-1617 - [c11]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 - [c10]Gellért Weisz, András György, Csaba Szepesvári:
Online RL in Linearly qπ-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. NeurIPS 2023 - [i10]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR abs/2302.12940 (2023) - [i9]Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári:
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR abs/2305.11032 (2023) - [i8]Gellért Weisz, András György, Csaba Szepesvári:
Online RL in Linearly qπ-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. CoRR abs/2310.07811 (2023) - 2022
- [c9]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: 1097-1137 - [c8]Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári:
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. NeurIPS 2022 - [i7]Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári:
Confident Approximate Policy Iteration for Efficient Local Planning in qπ-realizable MDPs. CoRR abs/2210.15755 (2022) - 2021
- [c7]Gellért Weisz, Philip Amortila, Csaba Szepesvári:
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. ALT 2021: 1237-1264 - [c6]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: 4355-4385 - [i6]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 abs/2102.02049 (2021) - [i5]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 abs/2110.02195 (2021) - 2020
- [c5]Tor Lattimore, Csaba Szepesvári, Gellért Weisz:
Learning with Good Feature Representations in Bandits and in RL with a Generative Model. ICML 2020: 5662-5670 - [c4]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 - [i4]Gellért Weisz, Philip Amortila, Csaba Szepesvári:
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. CoRR abs/2010.01374 (2020)
2010 – 2019
- 2019
- [c3]Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvári, Gellért Weisz:
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction. ICML 2019: 3692-3702 - [c2]Gellért Weisz, András György, Csaba Szepesvári:
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. ICML 2019: 6707-6715 - [i3]Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvári, Gellért Weisz:
Exploration-Enhanced POLITEX. CoRR abs/1908.10479 (2019) - 2018
- [j1]Gellért Weisz, Pawel Budzianowski
, Pei-Hao Su, Milica Gasic:
Sample Efficient Deep Reinforcement Learning for Dialogue Systems With Large Action Spaces. IEEE ACM Trans. Audio Speech Lang. Process. 26(11): 2083-2097 (2018) - [c1]Gellért Weisz, András György, Csaba Szepesvári:
LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. ICML 2018: 5254-5262 - [i2]Gellért Weisz, Pawel Budzianowski, Pei-Hao Su, Milica Gasic:
Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces. CoRR abs/1802.03753 (2018) - [i1]Gellért Weisz, András György, Csaba Szepesvári:
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration. CoRR abs/1807.00755 (2018)
Coauthor Index
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