Provably Efficient Algorithms for Large-Scale Reinforcement Learning

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The Open Research Data Pilot will be prepared and submitted to the European Commission

Publicaciones

Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization

Autores: Gergely Neu, Nneka Okolo
Publicado en: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Editor: Proceedings of Machine Learning Research

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits

Autores: Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
Publicado en: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Editor: NeurIPS foundation

Proximal Point Imitation Learning

Autores: Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
Publicado en: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Editor: NeurIPS foundation

Online learning with off-policy feedback

Autores: Germano Gabbianelli, Matteo Papini, Gergely Neu
Publicado en: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Editor: Proceedings of Machine Learning Research

Optimistic Planning by Regularized Dynamic Programming

Autores: Antoine Moulin, Gergely Neu
Publicado en: International Conference on Machine Learning (ICML 2022), 2023
Editor: Proceedings of Machine Learning Research

Generalization bounds via convex analysis

Autores: Gabor Lugosi, Gergely Neu
Publicado en: Proceedings of Thirty Fifth Conference on Learning Theory (COLT 2022), 2022
Editor: Proceedings of Machine Learning Research

Smoothing policies and safe policy gradients

Autores: Matteo Papini; Matteo Pirotta; Marcello Restelli
Publicado en: Machine Learning, Edición 111, 2022, Página(s) 4081–4137, ISSN 1573-0565
Editor: Springer
DOI: 10.1007/s10994-022-06232-6

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