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← Authors

Andrea Zanette

18 papers · 2018–2025 · 4 conferences · across top CS/AI conferences

Achievements

Jump to papers ↓
+9 more ↓ 🏃 Academic Marathon (7) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (4) 🐣 Hot Topic Early Bird
🏃 Academic Marathon (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏆 Keyword Champion (3) 🗃️ Keyword Collector (67) 💎 Century Club (18) 🔥 Unstoppable (8) 📈 Trend Setter ❓ The Questioner

Conferences

ICML (8) NIPS (8) AISTATS (1) COLT (1)

Top co-authors

Emma Brunskill (9) Alessandro Lazaric (4) Mykel J Kochenderfer (3) Ruiqi Zhang (2) Martin J. Wainwright (2) Martin J Wainwright (2) Sergey Levine (1) Yifei Zhou (1) Hanshi Sun (1) Huitao Yang (1)

Keywords

sample complexity (5) function approximation (5) reinforcement learning (5) regret bound (5) linear function approximation (4) markov decision process (3) bellman operator (3) value iteration (3) batch learning (2) offline reinforcement learning (2) contextual bandit (2) policy optimization (2) online learning (2) bellman error (2) linear methods (1) value function (1) model misspecification (1) bellman residual (1) policy learning (1) optimal policy (1)

Papers

Accelerating Unbiased LLM Evaluation via Synthetic Feedback ICML 2025 Fast Best-of-N Decoding via Speculative Rejection NIPS 2024 ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL ICML 2024 When is Realizability Sufficient for Off-Policy Reinforcement Learning? ICML 2023 Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data NIPS 2023 Stabilizing Q-learning with Linear Architectures for Provable Efficient Learning ICML 2022 Bellman Residual Orthogonalization for Offline Reinforcement Learning NIPS 2022 Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation COLT 2021 Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning NIPS 2021 Design of Experiments for Stochastic Contextual Linear Bandits NIPS 2021 Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL ICML 2021 Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration AISTATS 2020 Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration NIPS 2020 Limiting Extrapolation in Linear Approximate Value Iteration NIPS 2019 Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model NIPS 2019 Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds ICML 2019 Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs ICML 2018