Matteo Pirotta

Temporal Difference Flows ICML 2025 Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models ICLR 2025 Fast Imitation via Behavior Foundation Models ICLR 2024 Simple Ingredients for Offline Reinforcement Learning ICML 2024 Contextual bandits with concave rewards, and an application to fair ranking ICLR 2023 Layered State Discovery for Incremental Autonomous Exploration ICML 2023 On the Complexity of Representation Learning in Contextual Linear Bandits AISTATS 2023 Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path ALT 2023 Adaptive Multi-Goal Exploration AISTATS 2022 A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning ICLR 2022 Encrypted Linear Contextual Bandit AISTATS 2022 Privacy Amplification via Shuffling for Linear Contextual Bandits ALT 2022 Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees NeurIPS 2022 Top K Ranking for Multi-Armed Bandit with Noisy Evaluations AISTATS 2022 A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces AISTATS 2021 A Provably Efficient Sample Collection Strategy for Reinforcement Learning NeurIPS 2021 Gaussian Approximation for Bias Reduction in Q-Learning JMLR 2021 Kernel-Based Reinforcement Learning: A Finite-Time Analysis ICML 2021 Leveraging Good Representations in Linear Contextual Bandits ICML 2021 Local Differential Privacy for Regret Minimization in Reinforcement Learning NeurIPS 2021 Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection NeurIPS 2021 Safe Policy Iteration: A Monotonically Improving Approximate Policy Iteration Approach JMLR 2021 Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model ALT 2021 Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret NeurIPS 2021 Active Model Estimation in Markov Decision Processes UAI 2020 Adversarial Attacks on Linear Contextual Bandits NeurIPS 2020 An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits NeurIPS 2020 Conservative Exploration in Reinforcement Learning AISTATS 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration AISTATS 2020 Improved Algorithms for Conservative Exploration in Bandits AAAI 2020 Improved Sample Complexity for Incremental Autonomous Exploration in MDPs NeurIPS 2020 No-Regret Exploration in Goal-Oriented Reinforcement Learning ICML 2020 Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs NeurIPS 2019 Regret Bounds for Learning State Representations in Reinforcement Learning NeurIPS 2019 Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning ICML 2018 Importance Weighted Transfer of Samples in Reinforcement Learning ICML 2018 Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes NeurIPS 2018 Stochastic Variance-Reduced Policy Gradient ICML 2018 Adaptive Batch Size for Safe Policy Gradients NeurIPS 2017 Boosted Fitted Q-Iteration ICML 2017 Compatible Reward Inverse Reinforcement Learning NeurIPS 2017 Regret Minimization in MDPs with Options without Prior Knowledge NeurIPS 2017 Adaptive Step-Size for Policy Gradient Methods NeurIPS 2013 Safe Policy Iteration ICML 2013