IMPROVED SAMPLE COMPLEXITY FOR INCREMENTAL AUTONOMOUS EXPLORATION IN MDPS

von Tarbouriech, Jean
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 14 of 27 (2021)

AN ASYMPTOTICALLY OPTIMAL PRIMAL-DUAL INCREMENTAL ALGORITHM FOR CONTEXTUAL LINEAR BANDITS

von Tirinzoni, Andrea
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 2 of 27 (2021)

EXPLORATION BONUS FOR REGRET MINIMIZATION IN DISCRETE AND CONTINUOUS AVERAGE

von Qian, Jian
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 7 of 20 (2020)

A Provably Efficient Sample Collection Strategy for Reinforcement Learning

von Tarbouriech, Jean
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 10 of 36 (2022)

Local Differential Privacy for Regret Minimization in Reinforcement Learning

von Garcelon, Evrard
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 13 of 36 (2022)

ACTIVE MODEL ESTIMATION IN MARKOV DECISION PROCESSES

von TARBOURIECH, JEAN
Veröffentlicht in Conference on Uncertainty in Artificial Intelligence (36. : 2020 : Online) 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020) ; Volume 3 of 3 (2021)

NO-REGRET EXPLORATION IN GOAL-ORIENTED REINFORCEMENT LEARNING

von Tarbouriech, Jean
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 13 of 15 (2021)

LEVERAGING GOOD REPRESENTATIONS IN LINEAR CONTEXTUAL BANDITS

von PAPINI, MATTEO
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 11 of 16 (2022)

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees

von Tirinzoni, Andrea
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 4 of 50 (2023)

REGRET BOUNDS FOR LEARNING STATE REPRESENTATIONS IN REINFORCEMENT LEARNING

von Ortner, Ronald
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 16 of 20 (2020)

KERNEL-BASED REINFORCEMENT LEARNING: A FINITE-TIME ANALYSIS

von DOMINGUES, OMAR D.
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 4 of 16 (2022)

Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection

von Papini, Matteo
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 20 of 36 (2022)

Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret

von Tarbouriech, Jean
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 9 of 36 (2022)

ADVERSARIAL ATTACKS ON LINEAR CONTEXTUAL BANDITS

von Garcelon, Evrard
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 18 of 27 (2021)