Olivier Bousquet

According to our database, Olivier Bousquet authored at least 73 papers between 1999 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of two.

2023

Diagonalization Games.

Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran

Electron. Colloquium Comput. Complex., 2023

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Compositional Semantic Parsing with Large Language Models.

Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Fine-Grained Distribution-Dependent Learning Curves.

Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Differentially-Private Bayes Consistency.

Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer

CoRR, 2022

The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima.

Peter L. Bartlett, Philip M. Long, Olivier Bousquet

CoRR, 2022

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi

CoRR, 2022

Monotone Learning.

Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

A theory of universal learning.

Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Statistically Near-Optimal Hypothesis Selection.

Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, Shay Moran

Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

2020

Predicting Neural Network Accuracy from Weights.

Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Synthetic Data Generators - Sequential and Private.

Olivier Bousquet, Roi Livni, Shay Moran

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data.

Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet

Proceedings of the 8th International Conference on Learning Representations, 2020

Sharper Bounds for Uniformly Stable Algorithms.

Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy

Proceedings of the Conference on Learning Theory, 2020

Proper Learning, Helly Number, and an Optimal SVM Bound.

Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy

Proceedings of the Conference on Learning Theory, 2020

Precision-Recall Curves Using Information Divergence Frontiers.

Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Google Research Football: A Novel Reinforcement Learning Environment.

Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Fast classification rates without standard margin assumptions.

Olivier Bousquet, Nikita Zhivotovskiy

CoRR, 2019

The Visual Task Adaptation Benchmark.

Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

CoRR, 2019

Evaluating Generative Models Using Divergence Frontiers.

Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly

CoRR, 2019

Passing Tests without Memorizing: Two Models for Fooling Discriminators.

Olivier Bousquet, Roi Livni, Shay Moran

CoRR, 2019

Practical and Consistent Estimation of f-Divergences.

Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When can unlabeled data improve the learning rate?

Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner

Proceedings of the Conference on Learning Theory, 2019

The Optimal Approximation Factor in Density Estimation.

Olivier Bousquet, Daniel Kane, Shay Moran

Proceedings of the Conference on Learning Theory, 2019

2018

Gradient Descent Quantizes ReLU Network Features.

Hartmut Maennel, Olivier Bousquet, Sylvain Gelly

CoRR, 2018

Assessing Generative Models via Precision and Recall.

Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Are GANs Created Equal? A Large-Scale Study.

Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Wasserstein Auto-Encoders.

Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf

Proceedings of the 6th International Conference on Learning Representations, 2018

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

Critical Hyper-Parameters: No Random, No Cry.

Olivier Bousquet, Sylvain Gelly, Karol Kurach, Olivier Teytaud, Damien Vincent

CoRR, 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

AdaGAN: Boosting Generative Models.

Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Approximation and Convergence Properties of Generative Adversarial Learning.

Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2010

L'apprentissage statistique à grande échelle.

Léon Bottou, Olivier Bousquet

Monde des Util. Anal. Données, 2010

2009

Prototype Classification: Insights from Machine Learning.

Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf

Neural Comput., 2009

2007

Guest editorial: Learning theory.

Olivier Bousquet, André Elisseeff

Mach. Learn., 2007

Statistical properties of kernel principal component analysis.

Gilles Blanchard, Olivier Bousquet, Laurent Zwald

Mach. Learn., 2007

Combining PAC-Bayesian and Generic Chaining Bounds.

Jean-Yves Audibert, Olivier Bousquet

J. Mach. Learn. Res., 2007

The Tradeoffs of Large Scale Learning.

Léon Bottou, Olivier Bousquet

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Learning using Large Datasets.

Léon Bottou, Olivier Bousquet

Proceedings of the Mining Massive Data Sets for Security, 2007

2006

Cascade Evaluation of Clustering Algorithms.

Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet

Proceedings of the Machine Learning: ECML 2006, 2006

2005

Kernel Methods for Measuring Independence.

Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf

J. Mach. Learn. Res., 2005

Maximal margin classification for metric spaces.

Matthias Hein, Olivier Bousquet, Bernhard Schölkopf

J. Comput. Syst. Sci., 2005

Evaluating Predictive Uncertainty Challenge.

Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf

Proceedings of the Machine Learning Challenges, 2005

Joint Kernel Maps.

Jason Weston, Bernhard Schölkopf, Olivier Bousquet

Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

SSC: statistical subspace clustering.

Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet

Proceedings of the Extraction et gestion des connaissances (EGC'2005), 2005

Measuring Statistical Dependence with Hilbert-Schmidt Norms.

Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf

Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

Kernel Constrained Covariance for Dependence Measurement.

Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

Hilbertian Metrics and Positive Definite Kernels on Probability Measures.

Matthias Hein, Olivier Bousquet

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Kernel methods and their potential use in signal processing.

Fernando Pérez-Cruz, Olivier Bousquet

IEEE Signal Process. Mag., 2004

A Compression Approach to Support Vector Model Selection.

Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf

J. Mach. Learn. Res., 2004

Distance-Based Classification with Lipschitz Functions.

Ulrike von Luxburg, Olivier Bousquet

J. Mach. Learn. Res., 2004

Limits of Spectral Clustering.

Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Hilbertian Metrics on Probability Measures and Their Application in SVM?s.

Matthias Hein, Thomas Navin Lal, Olivier Bousquet

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case.

Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin

Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003

Feature selection and transduction for prediction of molecular bioactivity for drug design.

Jason Weston, Fernando Pérez-Cruz, Olivier Bousquet, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf

Bioinform., 2003

Ranking on Data Manifolds.

Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Learning with Local and Global Consistency.

Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Measure Based Regularization.

Olivier Bousquet, Olivier Chapelle, Matthias Hein

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

PAC-Bayesian Generic Chaining.

Jean-Yves Audibert, Olivier Bousquet

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Kernel methods and their applications to signal processing.

Olivier Bousquet, Fernando Pérez-Cruz

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

Maximal Margin Classification for Metric Spaces.

Matthias Hein, Olivier Bousquet

Proceedings of the Computational Learning Theory and Kernel Machines, 2003

Introduction to Statistical Learning Theory.

Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi

Proceedings of the Advanced Lectures on Machine Learning, 2003

Concentration Inequalities.

Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet

Proceedings of the Advanced Lectures on Machine Learning, 2003

2002

Choosing Multiple Parameters for Support Vector Machines.

Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee

Mach. Learn., 2002

Tracking a Small Set of Experts by Mixing Past Posteriors.

Olivier Bousquet, Manfred K. Warmuth

J. Mach. Learn. Res., 2002

Stability and Generalization.

Olivier Bousquet, André Elisseeff

J. Mach. Learn. Res., 2002

On the Complexity of Learning the Kernel Matrix.

Olivier Bousquet, Daniel J. L. Herrmann

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds.

Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko

Proceedings of the Computational Learning Theory, 2002

Localized Rademacher Complexities.

Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson

Proceedings of the Computational Learning Theory, 2002

2000

Algorithmic Stability and Generalization Performance.

Olivier Bousquet, André Elisseeff

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots.

Karthik Balakrishnan, Olivier Bousquet, Vasant G. Honavar

Adapt. Behav., 1999