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ORCIDAndrea Zanette
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2020 – today
- 2024
[c17]Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar:
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. ICML 2024- [i17]Ruiqi Zhang, Yuexiang Zhai, Andrea Zanette:
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement. CoRR abs/2402.15703 (2024) - [i16]Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, Aviral Kumar:
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. CoRR abs/2402.19446 (2024) - [i15]Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette:
Fast Best-of-N Decoding via Speculative Rejection. CoRR abs/2410.20290 (2024) - 2023
- [c16]Andrea Zanette:
When is Realizability Sufficient for Off-Policy Reinforcement Learning? ICML 2023: 40637-40668 - [c15]Ruiqi Zhang, Andrea Zanette:
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data. NeurIPS 2023 - [i14]Ruiqi Zhang, Andrea Zanette:
Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data. CoRR abs/2307.04354 (2023) - 2022
- [c14]Andrea Zanette, Martin J. Wainwright:
Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning. ICML 2022: 25920-25954 - [c13]Andrea Zanette, Martin J. Wainwright:
Bellman Residual Orthogonalization for Offline Reinforcement Learning. NeurIPS 2022 - [i13]Andrea Zanette, Martin J. Wainwright:
Bellman Residual Orthogonalization for Offline Reinforcement Learning. CoRR abs/2203.12786 (2022) - [i12]Andrea Zanette, Martin J. Wainwright:
Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning. CoRR abs/2206.00796 (2022) - [i11]Andrea Zanette:
When is Realizability Sufficient for Off-Policy Reinforcement Learning? CoRR abs/2211.05311 (2022) - 2021
- [c12]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. COLT 2021: 4473-4525 - [c11]Andrea Zanette:
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL. ICML 2021: 12287-12297 - [c10]Andrea Zanette, Martin J. Wainwright, Emma Brunskill:
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. NeurIPS 2021: 13626-13640 - [c9]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. NeurIPS 2021: 22720-22731 - [i10]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. CoRR abs/2103.12923 (2021) - [i9]Andrea Zanette, Kefan Dong, Jonathan N. Lee, Emma Brunskill:
Design of Experiments for Stochastic Contextual Linear Bandits. CoRR abs/2107.09912 (2021) - [i8]Andrea Zanette, Martin J. Wainwright, Emma Brunskill:
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. CoRR abs/2108.08812 (2021) - 2020
- [c8]Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. AISTATS 2020: 1954-1964 - [c7]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Learning Near Optimal Policies with Low Inherent Bellman Error. ICML 2020: 10978-10989 - [c6]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. NeurIPS 2020 - [i7]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Learning Near Optimal Policies with Low Inherent Bellman Error. CoRR abs/2003.00153 (2020) - [i6]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. CoRR abs/2008.07737 (2020) - [i5]Andrea Zanette:
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL. CoRR abs/2012.08005 (2020)
2010 – 2019
- 2019
- [c5]Andrea Zanette, Emma Brunskill:
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. ICML 2019: 7304-7312 - [c4]Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill:
Limiting Extrapolation in Linear Approximate Value Iteration. NeurIPS 2019: 5616-5625 - [c3]Andrea Zanette, Mykel J. Kochenderfer, Emma Brunskill:
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. NeurIPS 2019: 5626-5635 - [i4]Andrea Zanette, Emma Brunskill:
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. CoRR abs/1901.00210 (2019) - [i3]Andrea Zanette, David Brandfonbrener, Matteo Pirotta, Alessandro Lazaric:
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. CoRR abs/1911.00567 (2019) - [i2]Andrea Zanette, Emma Brunskill:
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. CoRR abs/1911.00954 (2019) - 2018
- [c2]Andrea Zanette, Emma Brunskill:
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. ICML 2018: 5732-5740 - [c1]Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer
:
Robust Super-Level Set Estimation Using Gaussian Processes. ECML/PKDD (2) 2018: 276-291 - [i1]Andrea Zanette, Junzi Zhang, Mykel J. Kochenderfer:
Robust Super-Level Set Estimation using Gaussian Processes. CoRR abs/1811.09977 (2018)
Coauthor Index
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