HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données. - Sylvain Gelly; Nicolas Bredèche; Michèle Sebag (CAP, 2005)

Taylor-based pseudo-metrics for random process fitting in dynamic programming: expected loss minimization and risk management. - Sylvain Gelly; Jérémie Mary; Olivier Teytaud (CAP, 2005)

Statistical asymptotic and non-asymptotic consistency of bayesian networks: convergence to the right structure and consistent probability estimates. - Sylvain Gelly; Olivier Teytaud (CAP, 2005)

Apprentissage statistique et programmation génétique: la croissance du code est-elle inévitable? - Sylvain Gelly; Olivier Teytaud; Nicolas Bredèche; Marc Schoenauer (CAP, 2005)

Inférence dans les HMM hiérarchiques et factorisés : changement de représentation vers le formalisme des Réseaux Bayésiens. - Sylvain Gelly; Michèle Sebag; Nicolas Bredèche (EGC (Ateliers), 2005)

A statistical learning theory approach of bloat. - Sylvain Gelly; Olivier Teytaud; Nicolas Bredèche; Marc Schoenauer (GECCO, 2005)

From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. - Sylvain Gelly; Nicolas Bredèche; Michèle Sebag (SARA, 2005)

Artificial Agents and Speculative Bubbles - Yann Semet; Sylvain Gelly; Marc Schoenauer; Michèle Sebag (CoRR, 2005)

Resource-Aware Parameterizations of EDA. - Sylvain Gelly; Olivier Teytaud; Christian Gagné 001 (IEEE Congress on Evolutionary Computation, 2006)

Learning for stochastic dynamic programming. - Sylvain Gelly; Jérémie Mary; Olivier Teytaud (ESANN, 2006)

General Lower Bounds for Evolutionary Algorithms. - Olivier Teytaud; Sylvain Gelly (PPSN, 2006)

On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy. - Olivier Teytaud; Sylvain Gelly; Jérémie Mary (PPSN, 2006)

Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning. - Sylvain Gelly; Olivier Teytaud (Rev. d'Intelligence Artif., 2006)

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint. - Sylvain Gelly; Olivier Teytaud; Nicolas Bredèche; Marc Schoenauer (Rev. d'Intelligence Artif., 2006)

Modifications of UCT and sequence-like simulations for Monte-Carlo Go. - Yizao Wang; Sylvain Gelly (CIG, 2007)

DCMA: yet another derandomization in covariance-matrix-adaptation. - Olivier Teytaud; Sylvain Gelly (GECCO, 2007)

Nonlinear programming in approximate dynamic programming - bang-bang solutions, stock-management and unsmooth penalties. - Olivier Teytaud; Sylvain Gelly (ICINCO-ICSO, 2007)

Active learning in regression, with application to stochastic dynamic programming. - Olivier Teytaud; Sylvain Gelly; Jérémie Mary (ICINCO-ICSO, 2007)

Combining online and offline knowledge in UCT. - Sylvain Gelly; David Silver (ICML, 2007)

Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime. - Sylvain Gelly; Sylvie Ruette; Olivier Teytaud (Evol. Comput., 2007)

Achieving Master Level Play in 9 x 9 Computer Go. - Sylvain Gelly; David Silver (AAAI, 2008)

The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning. - Sylvain Gelly; Jean-Baptiste Hoock; Arpad Rimmel; Olivier Teytaud; Y. Kalemkarian (ICINCO-ICSO, 2008)

A Statistical Learning Perspective of Genetic Programming. - Nur Merve Amil; Nicolas Bredèche; Christian Gagné 001; Sylvain Gelly; Marc Schoenauer; Olivier Teytaud (EuroGP, 2009)

Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo. Apprentissage et MC. - Guillaume Chaslot; L. Chatriot; Christophe Fiter; Sylvain Gelly; Jean-Baptiste Hoock; Julien Perez; Arpad Rimmel; Olivier Teytaud (Rev. d'Intelligence Artif., 2009)

Monte-Carlo tree search and rapid action value estimation in computer Go. - Sylvain Gelly; David Silver (Artif. Intell., 2011)

The grand challenge of computer Go: Monte Carlo tree search and extensions. - Sylvain Gelly; Levente Kocsis; Marc Schoenauer; Michèle Sebag; David Silver; Csaba Szepesvári; Olivier Teytaud (Commun. ACM, 2012)

AdaGAN: Boosting Generative Models. - Ilya O. Tolstikhin; Sylvain Gelly; Olivier Bousquet; Carl-Johann Simon-Gabriel; Bernhard Schölkopf (NIPS, 2017)

Toward Optimal Run Racing: Application to Deep Learning Calibration. - Olivier Bousquet; Sylvain Gelly; Karol Kurach; Marc Schoenauer; Michèle Sebag; Olivier Teytaud; Damien Vincent (CoRR, 2017)

Critical Hyper-Parameters: No Random, No Cry. - Olivier Bousquet; Sylvain Gelly; Karol Kurach; Olivier Teytaud; Damien Vincent (CoRR, 2017)

Better Text Understanding Through Image-To-Text Transfer. - Karol Kurach; Sylvain Gelly; Michal Jastrzebski; Philip Häusser; Olivier Teytaud; Damien Vincent; Olivier Bousquet (CoRR, 2017)

Clustering Meets Implicit Generative Models. - Francesco Locatello; Damien Vincent; Ilya O. Tolstikhin; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf (ICLR (Workshop), 2018)

Wasserstein Auto-Encoders. - Ilya O. Tolstikhin; Olivier Bousquet; Sylvain Gelly; Bernhard Schölkopf (ICLR, 2018)

Are GANs Created Equal? A Large-Scale Study. - Mario Lucic; Karol Kurach; Marcin Michalski; Sylvain Gelly; Olivier Bousquet (NeurIPS, 2018)

Assessing Generative Models via Precision and Recall. - Mehdi S. M. Sajjadi; Olivier Bachem; Mario Lucic; Olivier Bousquet; Sylvain Gelly (NeurIPS, 2018)

Gradient Descent Quantizes ReLU Network Features. - Hartmut Maennel; Olivier Bousquet; Sylvain Gelly (CoRR, 2018)

MemGEN: Memory is All You Need. - Sylvain Gelly; Karol Kurach; Marcin Michalski; Xiaohua Zhai (CoRR, 2018)

On Accurate Evaluation of GANs for Language Generation. - Stanislau Semeniuta; Aliaksei Severyn; Sylvain Gelly (CoRR, 2018)

Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem. - Hugo Penedones; Damien Vincent; Hartmut Maennel; Sylvain Gelly; Timothy A. Mann; André Barreto 001 (CoRR, 2018)

The GAN Landscape: Losses, Architectures, Regularization, and Normalization. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

A Case for Object Compositionality in Deep Generative Models of Images. - Sjoerd van Steenkiste; Karol Kurach; Sylvain Gelly (CoRR, 2018)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (CoRR, 2018)

Towards Accurate Generative Models of Video: A New Metric & Challenges. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

When can unlabeled data improve the learning rate? - Christina Göpfert; Shai Ben-David; Olivier Bousquet; Sylvain Gelly; Ilya O. Tolstikhin; Ruth Urner (COLT, 2019)

On Self Modulation for Generative Adversarial Networks. - Ting Chen; Mario Lucic; Neil Houlsby; Sylvain Gelly (ICLR (Poster), 2019)

Episodic Curiosity through Reachability. - Nikolay Savinov; Anton Raichuk; Damien Vincent; Raphaël Marinier; Marc Pollefeys; Timothy P. Lillicrap; Sylvain Gelly (ICLR (Poster), 2019)

FVD: A new Metric for Video Generation. - Thomas Unterthiner; Sjoerd van Steenkiste; Karol Kurach; Raphaël Marinier; Marcin Michalski; Sylvain Gelly (DGS@ICLR, 2019)

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. - Octavian Ganea; Sylvain Gelly; Gary Bécigneul; Aliaksei Severyn (ICML, 2019)

Parameter-Efficient Transfer Learning for NLP. - Neil Houlsby; Andrei Giurgiu; Stanislaw Jastrzebski; Bruna Morrone; Quentin de Laroussilhe; Andrea Gesmundo; Mona Attariyan; Sylvain Gelly (ICML, 2019)

A Large-Scale Study on Regularization and Normalization in GANs. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (ICML, 2019)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (ICML, 2019)

High-Fidelity Image Generation With Fewer Labels. - Mario Lucic; Michael Tschannen; Marvin Ritter; Xiaohua Zhai; Olivier Bachem; Sylvain Gelly (ICML, 2019)

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. - Carlos Riquelme; Hugo Penedones; Damien Vincent; Hartmut Maennel; Sylvain Gelly; Timothy A. Mann; André Barreto 001; Gergely Neu (NeurIPS, 2019)

Evaluating Generative Models Using Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (CoRR, 2019)

MULEX: Disentangling Exploitation from Exploration in Deep RL. - Lucas Beyer; Damien Vincent; Olivier Teboul; Sylvain Gelly; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

The Visual Task Adaptation Benchmark. - Xiaohua Zhai; Joan Puigcerver; Alexander Kolesnikov 003; Pierre Ruyssen; Carlos Riquelme; Mario Lucic; Josip Djolonga; André Susano Pinto; Maxim Neumann; Alexey Dosovitskiy; Lucas Beyer; Olivier Bachem; et al. (CoRR, 2019)

Semantic Bottleneck Scene Generation. - Samaneh Azadi; Michael Tschannen; Eric Tzeng; Sylvain Gelly; Trevor Darrell; Mario Lucic (CoRR, 2019)

Large Scale Learning of General Visual Representations for Transfer. - Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Joan Puigcerver; Jessica Yung; Sylvain Gelly; Neil Houlsby (CoRR, 2019)

Google Research Football: A Novel Reinforcement Learning Environment. - Karol Kurach; Anton Raichuk; Piotr Stanczyk; Michal Zajac 005; Olivier Bachem; Lasse Espeholt; Carlos Riquelme; Damien Vincent; Marcin Michalski; Olivier Bousquet; Sylvain Gelly (AAAI, 2020)

A Commentary on the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (AAAI, 2020)

Precision-Recall Curves Using Information Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (AISTATS, 2020)

Self-Supervised Learning of Video-Induced Visual Invariances. - Michael Tschannen; Josip Djolonga; Marvin Ritter; Aravindh Mahendran; Neil Houlsby; Sylvain Gelly; Mario Lucic (CVPR, 2020)

Big Transfer (BiT): General Visual Representation Learning. - Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Joan Puigcerver; Jessica Yung; Sylvain Gelly; Neil Houlsby (ECCV (5), 2020)

On Mutual Information Maximization for Representation Learning. - Michael Tschannen; Josip Djolonga; Paul K. Rubenstein; Sylvain Gelly; Mario Lucic (ICLR, 2020)

What Do Neural Networks Learn When Trained With Random Labels? - Hartmut Maennel; Ibrahim M. Alabdulmohsin; Ilya O. Tolstikhin; Robert J. N. Baldock; Olivier Bousquet; Sylvain Gelly; Daniel Keysers (NeurIPS, 2020)

On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation. - Nicolas Brosse; Carlos Riquelme; Alice Martin 001; Sylvain Gelly; Eric Moulines (CoRR, 2020)

Predicting Neural Network Accuracy from Weights. - Thomas Unterthiner; Daniel Keysers; Sylvain Gelly; Olivier Bousquet; Ilya O. Tolstikhin (CoRR, 2020)

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (CoRR, 2020)

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (J. Mach. Learn. Res., 2020)

Investigating object compositionality in Generative Adversarial Networks. - Sjoerd van Steenkiste; Karol Kurach; Jürgen Schmidhuber; Sylvain Gelly (Neural Networks, 2020)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. - Marcin Andrychowicz; Anton Raichuk; Piotr Stanczyk; Manu Orsini; Sertan Girgin; Raphaël Marinier; Léonard Hussenot; Matthieu Geist; Olivier Pietquin; Marcin Michalski; Sylvain Gelly; Olivier Bachem (ICLR, 2021)

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. - Alexey Dosovitskiy; Lucas Beyer; Alexander Kolesnikov 003; Dirk Weissenborn; Xiaohua Zhai; Thomas Unterthiner; Mostafa Dehghani 001; Matthias Minderer; Georg Heigold; Sylvain Gelly; Jakob Uszkoreit; Neil Houlsby (ICLR, 2021)

Scalable Transfer Learning with Expert Models. - Joan Puigcerver; Carlos Riquelme Ruiz; Basil Mustafa; Cédric Renggli; André Susano Pinto; Sylvain Gelly; Daniel Keysers; Neil Houlsby (ICLR, 2021)

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (NeurIPS Datasets and Benchmarks, 2021)

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. - Vincent Dumoulin; Neil Houlsby; Utku Evci; Xiaohua Zhai; Ross Goroshin; Sylvain Gelly; Hugo Larochelle (CoRR, 2021)

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. - Jessica Yung; Rob Romijnders; Alexander Kolesnikov 003; Lucas Beyer; Josip Djolonga; Neil Houlsby; Sylvain Gelly; Mario Lucic; Xiaohua Zhai (CoRR, 2021)