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Provably Efficient Algorithms for Large-Scale Reinforcement Learning

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Data Management Plan (DMP) (opens in new window)

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

Publications

Offline Primal-Dual Reinforcement Learning for Linear MDPs

Author(s): G. Gabbianelli, G. Neu, N. Okolo, M. Papini
Published in: Proceedings of the Twenty-seventh International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Publisher: Proceedings of Machine Learning Research

Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees (opens in new window)

Author(s): Tirinzoni A.; Papini M.; Touati A.; Lazaric A.; Pirotta M.
Published in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Publisher: NeurIPS foundation
DOI: 10.48550/arxiv.2210.13083

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

Author(s): Gergely Neu, Nneka Okolo
Published in: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Publisher: Proceedings of Machine Learning Research

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

Author(s): Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
Published in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Publisher: NeurIPS foundation

Nonstochastic Contextual Combinatorial Bandits

Author(s): L. Zierahn, D. van der Hoeven, N. Cesa-Bianchi, G. Neu
Published in: Proceedings of the Twenty-sixth International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Publisher: Proceedings of Machine Learning Research

Optimistic Information-Directed Sampling

Author(s): G. Neu, M. Papini, L. Schwartz
Published in: Proceedings of the 36th Annual Conference on Learning Theory (COLT), 2024
Publisher: Proceedings of Machine Learning Research

Dealing with Unbounded Gradients in Stochastic Saddle-Point Optimizaiton

Author(s): G. Neu, N. Okolo
Published in: Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
Publisher: Proceedings of Machine Learning Research

Proximal Point Imitation Learning

Author(s): Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
Published in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022
Publisher: NeurIPS foundation

Importance-Weighted Offline Learning Done Right

Author(s): G. Gabbianelli, G. Neu, M. Papini
Published in: Proceedings of the 34th International Conference on Algorithmic Learning Theory (ALT), 2024
Publisher: Proceedings of Machine Learning Research

Online learning with off-policy feedback

Author(s): Germano Gabbianelli, Matteo Papini, Gergely Neu
Published in: Proceedings of The 34th International Conference on Algorithmic Learning Theory (ALT 2023), 2023
Publisher: Proceedings of Machine Learning Research

First-and Second-Order Bounds for Adversarial Linear Contextual Bandits

Author(s): J. Olkhovskaya, J. Mayo, T. van Erven, G. Neu, C.-Y. Wei
Published in: Advances in Neural Information Processing Systems 36 (NeurIPS), 2023
Publisher: NeurIPS foundation

Optimistic Planning by Regularized Dynamic Programming

Author(s): Antoine Moulin, Gergely Neu
Published in: International Conference on Machine Learning (ICML 2022), 2023
Publisher: Proceedings of Machine Learning Research

Generalization bounds via convex analysis

Author(s): Gabor Lugosi, Gergely Neu
Published in: Proceedings of Thirty Fifth Conference on Learning Theory (COLT 2022), 2022
Publisher: Proceedings of Machine Learning Research

Adversarial Contextual Bandits Go Kernelized

Author(s): G. Neu, J. Olkhovskaya, S. Vakili
Published in: Proceedings of the 34th International Conference on Algorithmic Learning Theory (ALT), 2024
Publisher: Proceedings of Machine Learning Research

Smoothing policies and safe policy gradients (opens in new window)

Author(s): Matteo Papini; Matteo Pirotta; Marcello Restelli
Published in: Machine Learning, Issue 111, 2022, Page(s) 4081–4137, ISSN 1573-0565
Publisher: Springer
DOI: 10.1007/s10994-022-06232-6

A note on regularised NTK dynamics with an application to PAC-Bayesian training (opens in new window)

Author(s): Clerico, Eugenio; Guedj, Benjamin
Published in: Transactions on Machine Learning Research, 2024, ISSN 2835-8856
Publisher: Transactions on Machine Learning Research
DOI: 10.48550/arxiv.2312.13259

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