Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs. - Andrea Zanette; Emma Brunskill (ICML, 2018)

Robust Super-Level Set Estimation Using Gaussian Processes. - Andrea Zanette; Junzi Zhang; Mykel J. Kochenderfer (ECML/PKDD (2), 2018)

Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. - Andrea Zanette; Emma Brunskill (ICML, 2019)

Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model. - Andrea Zanette; Mykel J. Kochenderfer; Emma Brunskill (NeurIPS, 2019)

Limiting Extrapolation in Linear Approximate Value Iteration. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (NeurIPS, 2019)

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Matteo Pirotta; Alessandro Lazaric (CoRR, 2019)

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration. - Andrea Zanette; David Brandfonbrener; Emma Brunskill; Matteo Pirotta; Alessandro Lazaric (AISTATS, 2020)

Learning Near Optimal Policies with Low Inherent Bellman Error. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (ICML, 2020)

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration. - Andrea Zanette; Alessandro Lazaric; Mykel J. Kochenderfer; Emma Brunskill (NeurIPS, 2020)

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. - Andrea Zanette; Ching-An Cheng; Alekh Agarwal (COLT, 2021)

Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL. - Andrea Zanette (ICML, 2021)

Design of Experiments for Stochastic Contextual Linear Bandits. - Andrea Zanette; Kefan Dong; Jonathan N. Lee; Emma Brunskill (NeurIPS, 2021)

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning. - Andrea Zanette; Martin J. Wainwright; Emma Brunskill (NeurIPS, 2021)

Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning. - Andrea Zanette; Martin J. Wainwright (ICML, 2022)

Bellman Residual Orthogonalization for Offline Reinforcement Learning. - Andrea Zanette; Martin J. Wainwright (NeurIPS, 2022)

Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning. - Andrea Zanette; Martin J. Wainwright (CoRR, 2022)

When is Realizability Sufficient for Off-Policy Reinforcement Learning? - Andrea Zanette (ICML, 2023)

Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data. - Ruiqi Zhang; Andrea Zanette (NeurIPS, 2023)

ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. - Yifei Zhou; Andrea Zanette; Jiayi Pan; Sergey Levine; Aviral Kumar (ICML, 2024)

Fast Best-of-N Decoding via Speculative Rejection. - Hanshi Sun; Momin Haider; Ruiqi Zhang; Huitao Yang; Jiahao Qiu; Ming Yin 003; Mengdi Wang; Peter L. Bartlett; Andrea Zanette (NeurIPS, 2024)

Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement. - Ruiqi Zhang; Yuexiang Zhai; Andrea Zanette (CoRR, 2024)

Training Language Models to Reason Efficiently. - Daman Arora; Andrea Zanette (CoRR, 2025)

Accelerating Unbiased LLM Evaluation via Synthetic Feedback. - Zhaoyi Zhou; Yuda Song 001; Andrea Zanette (CoRR, 2025)

Can Large Reasoning Models Self-Train? - Sheikh Shafayat; Fahim Tajwar; Ruslan Salakhutdinov; Jeff G. Schneider; Andrea Zanette (CoRR, 2025)

SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning. - Ruiqi Zhang; Daman Arora; Song Mei; Andrea Zanette (CoRR, 2025)