Rate of convergence of crossover operators. - Yann Ollivier (Random Struct. Algorithms, 2003)

Finding Related Pages Using Green Measures: An Illustration with Wikipedia. - Yann Ollivier; Pierre Senellart (AAAI, 2007)

Some Small Cancellation Properties of Random Groups. - Yann Ollivier (Int. J. Algebra Comput., 2007)

Layer-wise learning of deep generative models - Ludovic Arnold; Yann Ollivier (CoRR, 2012)

A Curved Brunn-Minkowski Inequality on the Discrete Hypercube, Or: What Is the Ricci Curvature of the Discrete Hypercube? - Yann Ollivier; Cédric Villani (SIAM J. Discret. Math., 2012)

Objective improvement in information-geometric optimization. - Youhei Akimoto; Yann Ollivier (FOGA, 2013)

Information-Geometric Optimization: The Interest of Information Theory for Discrete and Continuous Optimization. - Yann Ollivier (GSI, 2013)

Persistent Contextual Neural Networks for learning symbolic data sequences. - Yann Ollivier (CoRR, 2013)

Riemannian metrics for neural networks - Yann Ollivier (CoRR, 2013)

Auto-encoders: reconstruction versus compression. - Yann Ollivier (CoRR, 2014)

Laplace's Rule of Succession in Information Geometry. - Yann Ollivier (GSI, 2015)

Speed learning on the fly. - Pierre-Yves Massé; Yann Ollivier (CoRR, 2015)

Training recurrent networks online without backtracking. - Yann Ollivier; Guillaume Charpiat (CoRR, 2015)

Practical Riemannian Neural Networks. - Gaétan Marceau-Caron; Yann Ollivier (CoRR, 2016)

Natural Langevin Dynamics for Neural Networks. - Gaétan Marceau-Caron; Yann Ollivier (GSI, 2017)

Unbiasing Truncated Backpropagation Through Time. - Corentin Tallec; Yann Ollivier (CoRR, 2017)

True Asymptotic Natural Gradient Optimization. - Yann Ollivier (CoRR, 2017)

Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. - Yann Ollivier; Ludovic Arnold; Anne Auger; Nikolaus Hansen (J. Mach. Learn. Res., 2017)

Unbiased Online Recurrent Optimization. - Corentin Tallec; Yann Ollivier (ICLR (Poster), 2018)

Can recurrent neural networks warp time? - Corentin Tallec; Yann Ollivier (ICLR (Poster), 2018)

Mixed batches and symmetric discriminators for GAN training. - Thomas Lucas 002; Corentin Tallec; Yann Ollivier; Jakob Verbeek (ICML, 2018)

The Description Length of Deep Learning models. - Léonard Blier; Yann Ollivier (NeurIPS, 2018)

Adversarial Vulnerability of Neural Networks Increases With Input Dimension. - Carl-Johann Simon-Gabriel; Yann Ollivier; Bernhard Schölkopf; Léon Bottou; David Lopez-Paz (CoRR, 2018)

Do Deep Learning Models Have Too Many Parameters? An Information Theory Viewpoint. - Léonard Blier; Yann Ollivier (CoRR, 2018)

Approximate Temporal Difference Learning is a Gradient Descent for Reversible Policies. - Yann Ollivier (CoRR, 2018)

Separable value functions across time-scales. - Joshua Romoff; Peter Henderson 002; Ahmed Touati; Yann Ollivier; Joelle Pineau; Emma Brunskill (ICML, 2019)

White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. - Alexandre Sablayrolles; Matthijs Douze; Cordelia Schmid; Yann Ollivier; Hervé Jégou (ICML, 2019)

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. - Carl-Johann Simon-Gabriel; Yann Ollivier; Léon Bottou; Bernhard Schölkopf; David Lopez-Paz (ICML, 2019)

Making Deep Q-learning methods robust to time discretization. - Corentin Tallec; Léonard Blier; Yann Ollivier (ICML, 2019)

Learning with Random Learning Rates. - Léonard Blier; Pierre Wolinski; Yann Ollivier (ECML/PKDD (2), 2019)

Separating value functions across time-scales. - Joshua Romoff; Peter Henderson 002; Ahmed Touati; Yann Ollivier; Emma Brunskill; Joelle Pineau (CoRR, 2019)

Interpreting a Penalty as the Influence of a Bayesian Prior. - Pierre Wolinski; Guillaume Charpiat; Yann Ollivier (CoRR, 2020)

Learning One Representation to Optimize All Rewards. - Ahmed Touati; Yann Ollivier (NeurIPS, 2021)

Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint. - Léonard Blier; Corentin Tallec; Yann Ollivier (CoRR, 2021)

Unbiased Methods for Multi-Goal Reinforcement Learning. - Léonard Blier; Yann Ollivier (CoRR, 2021)

Agnostic Physics-Driven Deep Learning. - Benjamin Scellier; Siddhartha Mishra; Yoshua Bengio; Yann Ollivier (CoRR, 2022)

Does Zero-Shot Reinforcement Learning Exist? - Ahmed Touati; Jérémy Rapin; Yann Ollivier (ICLR, 2023)

Fast Imitation via Behavior Foundation Models. - Matteo Pirotta; Andrea Tirinzoni; Ahmed Touati; Alessandro Lazaric; Yann Ollivier (ICLR, 2024)

Simple Ingredients for Offline Reinforcement Learning. - Edoardo Cetin; Andrea Tirinzoni; Matteo Pirotta; Alessandro Lazaric; Yann Ollivier; Ahmed Touati (ICML, 2024)

Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weighting. - Edoardo Cetin; Ahmed Touati; Yann Ollivier (CoRR, 2024)

Which Features are Best for Successor Features? - Yann Ollivier (CoRR, 2025)

Tackling the Zero-Shot Reinforcement Learning Loss Directly. - Yann Ollivier (CoRR, 2025)