Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Importance Weighted Transfer of Samples in Reinforcement Learning.

A. Tirinzoni, A. Sessa, M. Pirotta, and M. Restelli. ICML, volume 80 of Proceedings of Machine Learning Research, page 4943-4952. PMLR, (2018)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Matteo Garavoglia

Matteo Rini

Marco Matteo

Matteo Bertini

Matteo Gabba

Other publications of authors with the same name

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning.R. Fruit, M. Pirotta, A. Lazaric, and R. Ortner. ICML, volume 80 of Proceedings of Machine Learning Research, page 1573-1581. PMLR, (2018)Stochastic Variance-Reduced Policy Gradient.M. Papini, D. Binaghi, G. Canonaco, M. Pirotta, and M. Restelli. ICML, volume 80 of Proceedings of Machine Learning Research, page 4023-4032. PMLR, (2018)No-Regret Exploration in Goal-Oriented Reinforcement Learning.J. Tarbouriech, E. Garcelon, M. Valko, M. Pirotta, and A. Lazaric. CoRR, (2019)Inverse Reinforcement Learning through Policy Gradient Minimization.M. Pirotta, and M. Restelli. AAAI, page 1993-1999. AAAI Press, (2016)Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection.M. Papini, A. Tirinzoni, A. Pacchiano, M. Restelli, A. Lazaric, and M. Pirotta. NeurIPS, page 16371-16383. (2021)Leveraging Good Representations in Linear Contextual Bandits.M. Papini, A. Tirinzoni, M. Restelli, A. Lazaric, and M. Pirotta. ICML, volume 139 of Proceedings of Machine Learning Research, page 8371-8380. PMLR, (2021)On the Complexity of Representation Learning in Contextual Linear Bandits.A. Tirinzoni, M. Pirotta, and A. Lazaric. AISTATS, volume 206 of Proceedings of Machine Learning Research, page 7871-7896. PMLR, (2023)A Unified Framework for Conservative Exploration.Y. Yang, T. Wu, H. Zhong, E. Garcelon, M. Pirotta, A. Lazaric, L. Wang, and S. Du. CoRR, (2021)A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces.O. Domingues, P. Ménard, M. Pirotta, E. Kaufmann, and M. Valko. AISTATS, volume 130 of Proceedings of Machine Learning Research, page 3538-3546. PMLR, (2021)Kernel-Based Reinforcement Learning: A Finite-Time Analysis.O. Domingues, P. Ménard, M. Pirotta, E. Kaufmann, and M. Valko. ICML, volume 139 of Proceedings of Machine Learning Research, page 2783-2792. PMLR, (2021)

BibSonomy

Disambiguation of "Pirotta, Matteo"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Importance Weighted Transfer of Samples in Reinforcement Learning.

Please choose a person to relate this publication to

Matteo Garavoglia

Matteo Rini

Marco Matteo

Matteo Bertini

Matteo Gabba

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Pirotta, Matteo"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Importance Weighted Transfer of Samples in Reinforcement Learning.

Please choose a person to relate this publication to

Matteo Garavoglia

Matteo Rini

Marco Matteo

Matteo Bertini

Matteo Gabba

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Importance Weighted Transfer of Samples in Reinforcement Learning.