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Books and Theses
- 2022
[b1]Léonard Blier:
Sur certaines méthodes raisonnées pour l'apprentissage par renforcement profond. (Some Principled Methods for Deep Reinforcement Learning). University of Paris-Saclay, France, 2022
Conference and Workshop Papers
- 2019
- [c3]Corentin Tallec, Léonard Blier, Yann Ollivier:
Making Deep Q-learning methods robust to time discretization. ICML 2019: 6096-6104 - [c2]Léonard Blier, Pierre Wolinski, Yann Ollivier:
Learning with Random Learning Rates. ECML/PKDD (2) 2019: 449-464 - 2018
- [c1]Léonard Blier, Yann Ollivier:
The Description Length of Deep Learning models. NeurIPS 2018: 2220-2230
Informal and Other Publications
- 2021
- [i5]Léonard Blier, Corentin Tallec, Yann Ollivier:
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint. CoRR abs/2101.07123 (2021) - [i4]Léonard Blier, Yann Ollivier:
Unbiased Methods for Multi-Goal Reinforcement Learning. CoRR abs/2106.08863 (2021) - 2019
- [i3]Corentin Tallec, Léonard Blier, Yann Ollivier:
Making Deep Q-learning methods robust to time discretization. CoRR abs/1901.09732 (2019) - 2018
- [i2]Léonard Blier, Yann Ollivier:
Do Deep Learning Models Have Too Many Parameters? An Information Theory Viewpoint. CoRR abs/1802.07044 (2018) - [i1]Léonard Blier, Pierre Wolinski
, Yann Ollivier:
Learning with Random Learning Rates. CoRR abs/1810.01322 (2018)
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