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2020 – today
- 2024
[c18]Matteo Pirotta, Andrea Tirinzoni, Ahmed Touati, Alessandro Lazaric, Yann Ollivier:
Fast Imitation via Behavior Foundation Models. ICLR 2024- [c17]Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati:
Simple Ingredients for Offline Reinforcement Learning. ICML 2024 - [i30]Edoardo Cetin, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric, Yann Ollivier, Ahmed Touati:
Simple Ingredients for Offline Reinforcement Learning. CoRR abs/2403.13097 (2024) - [i29]Edoardo Cetin, Ahmed Touati, Yann Ollivier:
Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting. CoRR abs/2412.04368 (2024) - 2023
- [c16]Ahmed Touati, Jérémy Rapin, Yann Ollivier:
Does Zero-Shot Reinforcement Learning Exist? ICLR 2023 - 2022
- [i28]Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier:
Agnostic Physics-Driven Deep Learning. CoRR abs/2205.15021 (2022) - [i27]Ahmed Touati, Jérémy Rapin, Yann Ollivier:
Does Zero-Shot Reinforcement Learning Exist? CoRR abs/2209.14935 (2022) - 2021
- [c15]Ahmed Touati, Yann Ollivier:
Learning One Representation to Optimize All Rewards. NeurIPS 2021: 13-23 - [i26]Léonard Blier, Corentin Tallec, Yann Ollivier:
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint. CoRR abs/2101.07123 (2021) - [i25]Ahmed Touati, Yann Ollivier:
Learning One Representation to Optimize All Rewards. CoRR abs/2103.07945 (2021) - [i24]Léonard Blier, Yann Ollivier:
Unbiased Methods for Multi-Goal Reinforcement Learning. CoRR abs/2106.08863 (2021) - 2020
- [i23]Pierre Wolinski
, Guillaume Charpiat, Yann Ollivier:
Interpreting a Penalty as the Influence of a Bayesian Prior. CoRR abs/2002.00178 (2020)
2010 – 2019
- 2019
- [c14]Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill:
Separable value functions across time-scales. ICML 2019: 5468-5477 - [c13]Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Hervé Jégou:
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. ICML 2019: 5558-5567 - [c12]Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz:
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. ICML 2019: 5809-5817 - [c11]Corentin Tallec, Léonard Blier, Yann Ollivier:
Making Deep Q-learning methods robust to time discretization. ICML 2019: 6096-6104 - [c10]Léonard Blier, Pierre Wolinski, Yann Ollivier:
Learning with Random Learning Rates. ECML/PKDD (2) 2019: 449-464 - [i22]Corentin Tallec, Léonard Blier, Yann Ollivier:
Making Deep Q-learning methods robust to time discretization. CoRR abs/1901.09732 (2019) - [i21]Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Emma Brunskill, Joelle Pineau:
Separating value functions across time-scales. CoRR abs/1902.01883 (2019) - [i20]Alexandre Sablayrolles, Matthijs Douze, Yann Ollivier, Cordelia Schmid, Hervé Jégou:
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. CoRR abs/1908.11229 (2019) - 2018
- [c9]Corentin Tallec, Yann Ollivier:
Unbiased Online Recurrent Optimization. ICLR (Poster) 2018 - [c8]Corentin Tallec, Yann Ollivier:
Can recurrent neural networks warp time? ICLR (Poster) 2018 - [c7]Thomas Lucas, Corentin Tallec, Yann Ollivier, Jakob Verbeek:
Mixed batches and symmetric discriminators for GAN training. ICML 2018: 2850-2859 - [c6]Léonard Blier, Yann Ollivier:
The Description Length of Deep Learning models. NeurIPS 2018: 2220-2230 - [i19]Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Schölkopf, Léon Bottou, David Lopez-Paz:
Adversarial Vulnerability of Neural Networks Increases With Input Dimension. CoRR abs/1802.01421 (2018) - [i18]Léonard Blier, Yann Ollivier:
Do Deep Learning Models Have Too Many Parameters? An Information Theory Viewpoint. CoRR abs/1802.07044 (2018) - [i17]Corentin Tallec, Yann Ollivier:
Can recurrent neural networks warp time? CoRR abs/1804.11188 (2018) - [i16]Yann Ollivier:
Approximate Temporal Difference Learning is a Gradient Descent for Reversible Policies. CoRR abs/1805.00869 (2018) - [i15]Thomas Lucas, Corentin Tallec, Jakob Verbeek, Yann Ollivier:
Mixed batches and symmetric discriminators for GAN training. CoRR abs/1806.07185 (2018) - [i14]Léonard Blier, Pierre Wolinski, Yann Ollivier:
Learning with Random Learning Rates. CoRR abs/1810.01322 (2018) - 2017
- [j4]Yann Ollivier, Ludovic Arnold, Anne Auger, Nikolaus Hansen:
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. J. Mach. Learn. Res. 18: 18:1-18:65 (2017) - [c5]Gaétan Marceau-Caron, Yann Ollivier:
Natural Langevin Dynamics for Neural Networks. GSI 2017: 451-459 - [i13]Corentin Tallec, Yann Ollivier:
Unbiased Online Recurrent Optimization. CoRR abs/1702.05043 (2017) - [i12]Corentin Tallec, Yann Ollivier:
Unbiasing Truncated Backpropagation Through Time. CoRR abs/1705.08209 (2017) - [i11]Gaétan Marceau-Caron, Yann Ollivier:
Natural Langevin Dynamics for Neural Networks. CoRR abs/1712.01076 (2017) - [i10]Yann Ollivier:
True Asymptotic Natural Gradient Optimization. CoRR abs/1712.08449 (2017) - 2016
- [i9]Gaétan Marceau-Caron, Yann Ollivier:
Practical Riemannian Neural Networks. CoRR abs/1602.08007 (2016) - 2015
- [c4]Yann Ollivier:
Laplace's Rule of Succession in Information Geometry. GSI 2015: 311-319 - [i8]Yann Ollivier:
Laplace's rule of succession in information geometry. CoRR abs/1503.04304 (2015) - [i7]Yann Ollivier, Guillaume Charpiat:
Training recurrent networks online without backtracking. CoRR abs/1507.07680 (2015) - [i6]Pierre-Yves Massé, Yann Ollivier:
Speed learning on the fly. CoRR abs/1511.02540 (2015) - 2014
- [e1]Yann Ollivier, Herve Pajot, Cédric Villani:
Optimal Transport - Theory and Applications. London Mathematical Society lecture note series 413, Cambridge University Press 2014, ISBN 978-1-10-768949-7 [contents] - [i5]Yann Ollivier:
Auto-encoders: reconstruction versus compression. CoRR abs/1403.7752 (2014) - 2013
- [c3]Youhei Akimoto, Yann Ollivier:
Objective improvement in information-geometric optimization. FOGA 2013: 1-10 - [c2]Yann Ollivier:
Information-Geometric Optimization: The Interest of Information Theory for Discrete and Continuous Optimization. GSI 2013: 4 - [i4]Yann Ollivier:
Riemannian metrics for neural networks. CoRR abs/1303.0818 (2013) - [i3]Yann Ollivier:
Persistent Contextual Neural Networks for learning symbolic data sequences. CoRR abs/1306.0514 (2013) - 2012
- [j3]Yann Ollivier, Cédric Villani:
A Curved Brunn-Minkowski Inequality on the Discrete Hypercube, Or: What Is the Ricci Curvature of the Discrete Hypercube? SIAM J. Discret. Math. 26(3): 983-996 (2012) - [i2]Youhei Akimoto, Yann Ollivier:
Objective Improvement in Information-Geometric Optimization. CoRR abs/1211.3831 (2012) - [i1]Ludovic Arnold, Yann Ollivier:
Layer-wise learning of deep generative models. CoRR abs/1212.1524 (2012)
2000 – 2009
- 2007
- [j2]Yann Ollivier:
Some Small Cancellation Properties of Random Groups. Int. J. Algebra Comput. 17(1): 37-51 (2007) - [c1]Yann Ollivier, Pierre Senellart:
Finding Related Pages Using Green Measures: An Illustration with Wikipedia. AAAI 2007: 1427-1433 - 2003
- [j1]Yann Ollivier:
Rate of convergence of crossover operators. Random Struct. Algorithms 23(1): 58-72 (2003)
Coauthor Index
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