S. Gelly

发表

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Achieving Master Level Play in 9 × 9 Computer Go

David Silver, Sylvain Gelly, D. Silver, 2022 .

Anytime many-armed bandits

S. Gelly, O. Teytaud, M. Sebag, 2007 .

Combining online and offline knowledge in UCT

David Silver, Sylvain Gelly, D. Silver, 2007, ICML '07.

Monte-Carlo tree search and rapid action value estimation in computer Go

David Silver, Sylvain Gelly, D. Silver, 2011, Artif. Intell..

Combining expert, offline, transient and online knowledge in Monte-Carlo exploration

S. Gelly, O. Teytaud, Guillaume Chaslot, 2008 .

Critical Hyper-Parameters: No Random, No Cry pdf

Olivier Teytaud, Sylvain Gelly, Olivier Bousquet, 2017, ArXiv.

From optimal transport to generative modeling: the VEGAN cookbook

O. Bousquet, S. Gelly, B. Schoelkopf, 2017, 1705.07642.

The grand challenge of computer Go

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2012, Commun. ACM.

Are GANs Created Equal? A Large-Scale Study

Mario Lucic, Sylvain Gelly, Olivier Bousquet, 2017, NeurIPS.

Modification of UCT with Patterns in Monte-Carlo Go

Olivier Teytaud, Sylvain Gelly, Rémi Munos, 2006 .

Predicting Neural Network Accuracy from Weights

O. Bousquet, S. Gelly, Thomas Unterthiner, 2020, ArXiv.

Combining Online and Offline Learning in UCT

David Silver, S Gelly, D. Silver, 2007 .

Modifications of UCT and sequence-like simulations for Monte-Carlo Go

Sylvain Gelly, Yizao Wang, S. Gelly, 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

Exploration exploitation in Go: UCT for Monte-Carlo Go

Sylvain Gelly, Yizao Wang, S. Gelly, 2006, NIPS 2006.

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations pdf

Bernhard Schölkopf, Francesco Locatello, Olivier Bachem, 2018, ICML.

Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in EDA, and implementations with billiards

S. Gelly, O. Teytaud, Sylvie Ruette, 2006 .

AdaGAN: Boosting Generative Models

Bernhard Schölkopf, Sylvain Gelly, Olivier Bousquet, 2017, NIPS.

Gradient Descent Quantizes ReLU Network Features pdf

Sylvain Gelly, Hartmut Maennel, Olivier Bousquet, 2018, ArXiv.

The GAN Landscape: Losses, Architectures, Regularization, and Normalization pdf

Xiaohua Zhai, Mario Lucic, Sylvain Gelly, 2018, ArXiv.

Episodic Curiosity through Reachability pdf

Marc Pollefeys, Nikolay Savinov, Timothy Lillicrap, 2018, ICLR.

Active learning in regression, with application to stochastic dynamic programming

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2007, ICINCO-ICSO.

General Lower Bounds for Evolutionary Algorithms

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2006, PPSN.

Google Research Football: A Novel Reinforcement Learning Environment

O. Bousquet, S. Gelly, Lasse Espeholt, 2019, AAAI.

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

S. Gelly, Karol Kurach, Mario Lucic, 2018, ArXiv.

Self-Supervised Learning of Video-Induced Visual Invariances

S. Gelly, M. Tschannen, Josip Djolonga, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Symbolic regression, parsimony, and some theoretical considerations about GP search space

S. Gelly, O. Teytaud, Marc Schoenauer, 2005 .

MULEX: Disentangling Exploitation from Exploration in Deep RL

S. Gelly, Damien Vincent, O. Pietquin, 2019, ArXiv.

What Do Neural Networks Learn When Trained With Random Labels?

Ibrahim M. Alabdulmohsin, O. Bousquet, S. Gelly, 2020, NeurIPS.

A N I MAGE IS W ORTH 16 X 16 W ORDS : T RANSFORMERS FOR I MAGE R ECOGNITION AT S CALE

S. Gelly, A. Dosovitskiy, Thomas Unterthiner, 2021 .

On the Parallelization of Monte-Carlo planning

Olivier Teytaud, Sylvain Gelly, Arpad Rimmel, 2008, ICINCO 2008.

Derandomized sampling for learning , with application to stochastic dynamic programming

Jérémie Mary, S. Gelly, O. Teytaud, 2007 .

Agent-Based Computational Economics and Finance: early research and design issues

S. Gelly, Marc Schoenauer, Y. Semet, 2003 .

Parameter-Efficient Transfer Learning for NLP

Mona Attariyan, Stanislaw Jastrzebski, Neil Houlsby, 2019, ICML.

Non linear programming for stochastic dynamic programming

S. Gelly, O. Teytaud, 2007 .

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint

Olivier Teytaud, Marc Schoenauer, Nicolas Bredeche, 2006, Rev. d'Intelligence Artif..

Bayesian networks : a better than frequentist approach for parametrization, and a more accurate structural complexity measure than the number of parameters

S. Gelly, O. Teytaud, 2005 .

Quasi-random resamplings , with applications to rule extraction , cross-validation and ( su-) bagging

S. Gelly, O. Teytaud, Elie Prudhomme, 2006 .

Quasi-Random resamplings, with applications to rule-samplng, cross-validation and (su-)bagging

S. Gelly, O. Teytaud, S. Lallich, 2006 .

Google Research Football: A Novel Reinforcement Learning Environment

O. Bousquet, S. Gelly, Lasse Espeholt, 2019, AAAI.

The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions

S. Gelly, O. Teytaud, M. Sebag, 2015 .

New Results - Machine Learning for Stochastic Optimisation

Michèle Sebag, Olivier Teytaud, Anne Auger, 2007 .

On Invariances in Evolutionary Algorithms

S. Gelly, O. Teytaud, A. Auger, 2006 .

Funnel Vision Transformer for image classification

S. Gelly, A. Dosovitskiy, Thomas Unterthiner, 2022 .

TD Learning with Neural Networks - Study of the Leakage Propagation Problem

Timothy A. Mann, Sylvain Gelly, Damien Vincent, 2018 .

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

S. Gelly, A. Dosovitskiy, Thomas Unterthiner, 2020, ICLR.

Big Transfer (BiT): General Visual Representation Learning

S. Gelly, Alexander Kolesnikov, Xiaohua Zhai, 2019, ECCV.

The Parallelization of Monte-Carlo Planning - Parallelization of MC-Planning

Olivier Teytaud, Sylvain Gelly, Arpad Rimmel, 2008, ICINCO-ICSO.

Learning for stochastic dynamic programming

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2006, ESANN.

Robust Optimizers for Nonlinear Programming in Approximate Dynamic Programming

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2009 .

DCMA: yet another derandomization in covariance-matrix-adaptation

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2007, GECCO '07.

Non linear programming for stochastic dynamic programming

S. Gelly, O. Teytaud, 2007 .

Nonlinear programming in approximate dynamic programming - bang-bang solutions, stock-management and unsmooth penalties

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2007, ICINCO-ICSO.

On the Ultimate Convergence Rates for Isotropic Algorithms and the Best Choices Among Various Forms of Isotropy

Olivier Teytaud, Sylvain Gelly, Jérémie Mary, 2006, PPSN.

Change Point Detection and Meta-Bandits for Online Learning in Dynamic Environments

Michèle Sebag, Olivier Teytaud, Cédric Hartland, 2007 .

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation pdf

Francesco Locatello, Olivier Bachem, Mario Lucic, 2020, J. Mach. Learn. Res..

Assessing Generative Models via Precision and Recall

Olivier Bachem, Mario Lucic, Sylvain Gelly, 2018, NeurIPS.

Precision-Recall Curves Using Information Divergence Frontiers

Olivier Bachem, Marco Cuturi, Mario Lucic, 2019, AISTATS.

Investigating object compositionality in Generative Adversarial Networks

Sjoerd van Steenkiste, J. Schmidhuber, S. Gelly, 2018, Neural Networks.

FVD: A new Metric for Video Generation

Sjoerd van Steenkiste, Raphaël Marinier, Sylvain Gelly, 2019, DGS@ICLR.

What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study pdf

Marcin Andrychowicz, Matthieu Geist, Olivier Pietquin, 2020, ArXiv.

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities

Gary Bécigneul, Sylvain Gelly, Octavian-Eugen Ganea, 2019, ICML.

On Self Modulation for Generative Adversarial Networks pdf

Ting Chen, Mario Lucic, Neil Houlsby, 2018, ICLR.

MULEX: Disentangling Exploitation from Exploration in Deep RL pdf

Lucas Beyer, Matthieu Geist, Olivier Pietquin, 2019, ArXiv.

On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation pdf

Sylvain Gelly, Nicolas Brosse, Eric Moulines, 2020, ArXiv.

On Robustness and Transferability of Convolutional Neural Networks pdf

Alexander D'Amour, Xiaohua Zhai, Lucas Beyer, 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Scalable Transfer Learning with Expert Models pdf

Joan Puigcerver, Neil Houlsby, Daniel Keysers, 2020, ICLR.

Towards Accurate Generative Models of Video: A New Metric & Challenges pdf

Sjoerd van Steenkiste, Raphaël Marinier, Sylvain Gelly, 2018, ArXiv.

A Large-Scale Study on Regularization and Normalization in GANs

Xiaohua Zhai, Mario Lucic, Sylvain Gelly, 2018, ICML.

Predicting Neural Network Accuracy from Weights pdf

Daniel Keysers, Olivier Bousquet, Sylvain Gelly, 2020, ArXiv.

A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

André Susano Pinto, O. Bousquet, S. Gelly, 2019, 1910.04867.

On Mutual Information Maximization for Representation Learning pdf

Michael Tschannen, Mario Lucic, Sylvain Gelly, 2019, ICLR.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study

Matthieu Geist, Olivier Pietquin, Raphaël Marinier, 2021, ICLR.

Large Scale Learning of General Visual Representations for Transfer

S. Gelly, Xiaohua Zhai, L. Beyer, 2019, ArXiv.

On Accurate Evaluation of GANs for Language Generation pdf

Stanislau Semeniuta, Sylvain Gelly, Aliaksei Severyn, 2018, ArXiv.

A Statistical Learning Perspective of Genetic Programming

Christian Gagné, Olivier Teytaud, Marc Schoenauer, 2009, EuroGP.

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint

Olivier Teytaud, Marc Schoenauer, Sylvain Gelly, 2006, Rev. d'Intelligence Artif..

A statistical learning theory approach of bloat

Olivier Teytaud, Marc Schoenauer, Sylvain Gelly, 2005, GECCO '05.

High-Fidelity Image Generation With Fewer Labels pdf

Xiaohua Zhai, Olivier Bachem, Michael Tschannen, 2019, ICML.

Artificial Agents and Speculative Bubbles pdf

Michèle Sebag, Marc Schoenauer, Sylvain Gelly, 2004, ArXiv.

Semantic Bottleneck Scene Generation pdf

Trevor Darrell, Michael Tschannen, Mario Lucic, 2019, ArXiv.

Multi-armed Bandit, Dynamic Environments and Meta-Bandits

Michèle Sebag, Olivier Teytaud, Cédric Hartland, 2006 .

A Case for Object Compositionality in Deep Generative Models of Images pdf

Sjoerd van Steenkiste, Sylvain Gelly, Karol Kurach, 2018, ArXiv.

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark pdf

Xiaohua Zhai, Hugo Larochelle, Neil Houlsby, 2021, ArXiv.

Monte-Carlo Tree Search

Olivier Teytaud, Sylvain Gelly, Romaric Gaudel, 2012 .

The Visual Task Adaptation Benchmark

André Susano Pinto, O. Bousquet, S. Gelly, 2019, ArXiv.

A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches

H. Larochelle, S. Gelly, Vincent Dumoulin, 2021, NeurIPS Datasets and Benchmarks.

When can unlabeled data improve the learning rate?

Shai Ben-David, Ruth Urner, Olivier Bousquet, 2019, COLT.

A Commentary on the Unsupervised Learning of Disentangled Representations pdf

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2020, AAAI.

Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure Bayesian Networks Learning

Olivier Teytaud, Sylvain Gelly, S. Gelly, 2006, Rev. d'Intelligence Artif..

Parameter-Efficient Transfer Learning for NLP

Quentin de Laroussilhe, S. Gelly, Stanislaw Jastrzebski, 2019, ICML.

Comparison-Based Algorithms Are Robust and Randomized Algorithms Are Anytime

Olivier Teytaud, Sylvain Gelly, Sylvie Ruette, 2007, Evolutionary Computation.

MemGEN: Memory is All You Need pdf

Xiaohua Zhai, Sylvain Gelly, Karol Kurach, 2018, ArXiv.

Big Transfer (BiT): General Visual Representation Learning

S. Gelly, Alexander Kolesnikov, Xiaohua Zhai, 2019, ECCV.

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size pdf

Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer, 2021, ArXiv.

Precision-Recall Curves Using Information Divergence Frontiers

O. Bousquet, S. Gelly, Marco Cuturi, 2019, AISTATS.

Evaluating Generative Models Using Divergence Frontiers pdf

Olivier Bachem, Marco Cuturi, Mario Lucic, 2019, ArXiv.