| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1266 | 2025 |
| Learning with good feature representations in bandits and in rl with a generative model T Lattimore, C Szepesvari, G Weisz International conference on machine learning, 5662-5670, 2020 | 241 | 2020 |
| Politex: Regret bounds for policy iteration using expert prediction Y Abbasi-Yadkori, P Bartlett, K Bhatia, N Lazic, C Szepesvari, G Weisz International Conference on Machine Learning, 3692-3702, 2019 | 186 | 2019 |
| Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions G Weisz, P Amortila, C Szepesvári Algorithmic Learning Theory, 1237-1264, 2021 | 118 | 2021 |
| Sample efficient deep reinforcement learning for dialogue systems with large action spaces G Weisz, P Budzianowski, PH Su, M Gašić IEEE/ACM Transactions on Audio, Speech, and Language Processing 26 (11 …, 2018 | 106 | 2018 |
| LeapsAndBounds: A method for approximately optimal algorithm configuration G Weisz, A Gyorgy, C Szepesvári International Conference on Machine Learning, 5257-5265, 2018 | 50 | 2018 |
| Exploration-enhanced politex Y Abbasi-Yadkori, N Lazic, C Szepesvari, G Weisz arXiv preprint arXiv:1908.10479, 2019 | 39 | 2019 |
| On query-efficient planning in mdps under linear realizability of the optimal state-value function G Weisz, P Amortila, B Janzer, Y Abbasi-Yadkori, N Jiang, C Szepesvári Conference on Learning Theory, 4355-4385, 2021 | 31 | 2021 |
| CapsAndRuns: An improved method for approximately optimal algorithm configuration G Weisz, A Gyorgy, C Szepesvári International Conference on Machine Learning, 6707-6715, 2019 | 30 | 2019 |
| Optimistic natural policy gradient: a simple efficient policy optimization framework for online rl Q Liu, G Weisz, A György, C Jin, C Szepesvári Advances in Neural Information Processing Systems 36, 3560-3577, 2023 | 20 | 2023 |
| Online RL in Linearly -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore G Weisz, A György, C Szepesvári Advances in Neural Information Processing Systems 36, 59172-59205, 2023 | 19 | 2023 |
| Confident Approximate Policy Iteration for Efficient Local Planning in -realizable MDPs G Weisz, A György, T Kozuno, C Szepesvári Advances in Neural Information Processing Systems 35, 25547-25559, 2022 | 19 | 2022 |
| Tensorplan and the few actions lower bound for planning in mdps under linear realizability of optimal value functions G Weisz, C Szepesvári, A György International Conference on Algorithmic Learning Theory, 1097-1137, 2022 | 17 | 2022 |
| Exponential hardness of reinforcement learning with linear function approximation S Liu, G Mahajan, D Kane, S Lovett, G Weisz, C Szepesvári The Thirty Sixth Annual Conference on Learning Theory, 1588-1617, 2023 | 11* | 2023 |
| ImpatientCapsAndRuns: Approximately optimal algorithm configuration from an infinite pool G Weisz, A György, WI Lin, D Graham, K Leyton-Brown, C Szepesvari, ... Advances in Neural Information Processing Systems 33, 17478-17488, 2020 | 9 | 2020 |
| Inter-device data transfer based on barcodes J Chien, R Ian Orton, G Weisz, V Varma US Patent 9,600,701, 2017 | 9 | 2017 |
| Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear -Realizability and Concentrability V Tkachuk, G Weisz, C Szepesvári Advances in Neural Information Processing Systems 37, 83268-83313, 2024 | 4 | 2024 |
| The Complexity of Reinforcement Learning with Linear Function Approximation G Weisz UCL (Univesity College London), 2024 | | 2024 |
| P: Regret Bounds for Policy Iteration Using Expert Prediction Y Abbasi-Yadkori, PL Bartle, K Bhatia, N Lazić, C Szepesvári, G Weisz | | |
