Olivier Bousquet

Publication Activity (10 Years)

Years Active: 1999-2023
Publications (10 Years): 54

Top Topics

Generative Model

Learning Curves

Top Venues

Publications

Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran
Diagonalization Games. Electron. Colloquium Comput. Complex. (2023)
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. COLT (2023)
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ICLR (2023)
Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran
Diagonalization Games. CoRR (2023)
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou
Compositional Semantic Parsing with Large Language Models. ICLR (2023)
Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
Differentially-Private Bayes Consistency. CoRR (2022)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. COLT (2022)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. CoRR (2022)
Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, Shay Moran
Statistically Near-Optimal Hypothesis Selection. FOCS (2022)
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. CoRR (2022)
Peter L. Bartlett, Philip M. Long, Olivier Bousquet
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. CoRR (2022)
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. CoRR (2022)
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou
Compositional Semantic Parsing with Large Language Models. CoRR (2022)
Olivier Bousquet, Mark Braverman, Klim Efremenko, Gillat Kol, Shay Moran
Statistically Near-Optimal Hypothesis Selection. CoRR (2021)
Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko, Shay Moran
Statistically Near-Optimal Hypothesis Selection. FOCS (2021)
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
A theory of universal learning. STOC (2021)
Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff
A Theory of Universal Learning. CoRR (2020)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS (2020)
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin
Predicting Neural Network Accuracy from Weights. CoRR (2020)
Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT (2020)
Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
Google Research Football: A Novel Reinforcement Learning Environment. AAAI (2020)
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
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. ICLR (2020)
Olivier Bousquet, Roi Livni, Shay Moran
Synthetic Data Generators - Sequential and Private. NeurIPS (2020)
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
What Do Neural Networks Learn When Trained With Random Labels? NeurIPS (2020)
Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy
Sharper Bounds for Uniformly Stable Algorithms. COLT (2020)
Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR (2020)
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
What Do Neural Networks Learn When Trained With Random Labels? CoRR (2020)
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
Practical and Consistent Estimation of f-Divergences. NeurIPS (2019)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Evaluating Generative Models Using Divergence Frontiers. CoRR (2019)
Olivier Bousquet, Nikita Zhivotovskiy
Fast classification rates without standard margin assumptions. CoRR (2019)
Olivier Bousquet, Roi Livni, Shay Moran
Passing Tests without Memorizing: Two Models for Fooling Discriminators. CoRR (2019)
Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly
Google Research Football: A Novel Reinforcement Learning Environment. CoRR (2019)
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
The Visual Task Adaptation Benchmark. CoRR (2019)
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
Practical and Consistent Estimation of f-Divergences. CoRR (2019)
Olivier Bousquet, Daniel Kane, Shay Moran
The Optimal Approximation Factor in Density Estimation. CoRR (2019)
Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy
Sharper bounds for uniformly stable algorithms. CoRR (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? CoRR (2019)
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
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. CoRR (2019)
Olivier Bousquet, Daniel Kane, Shay Moran
The Optimal Approximation Factor in Density Estimation. COLT (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? COLT (2019)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. CoRR (2018)
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Are GANs Created Equal? A Large-Scale Study. NeurIPS (2018)
Hartmut Maennel, Olivier Bousquet, Sylvain Gelly
Gradient Descent Quantizes ReLU Network Features. CoRR (2018)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. NeurIPS (2018)
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
Wasserstein Auto-Encoders. ICLR (2018)
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
AdaGAN: Boosting Generative Models. CoRR (2017)
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Approximation and Convergence Properties of Generative Adversarial Learning. CoRR (2017)
Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent
Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR (2017)
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
Wasserstein Auto-Encoders. CoRR (2017)
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Approximation and Convergence Properties of Generative Adversarial Learning. NIPS (2017)
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
AdaGAN: Boosting Generative Models. NIPS (2017)
Karol Kurach, Sylvain Gelly, Michal Jastrzebski, Philip Häusser, Olivier Teytaud, Damien Vincent, Olivier Bousquet
Better Text Understanding Through Image-To-Text Transfer. CoRR (2017)
Olivier Bousquet, Sylvain Gelly, Karol Kurach, Olivier Teytaud, Damien Vincent
Critical Hyper-Parameters: No Random, No Cry. CoRR (2017)
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Are GANs Created Equal? A Large-Scale Study. CoRR (2017)
Léon Bottou, Olivier Bousquet
L'apprentissage statistique à grande échelle. Monde des Util. Anal. Données 42 (2010)
Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf
Prototype Classification: Insights from Machine Learning. Neural Comput. 21 (1) (2009)
Jean-Yves Audibert, Olivier Bousquet
Combining PAC-Bayesian and Generic Chaining Bounds. J. Mach. Learn. Res. 8 (2007)
Léon Bottou, Olivier Bousquet
The Tradeoffs of Large Scale Learning. NIPS (2007)
Léon Bottou, Olivier Bousquet
Learning using Large Datasets. NATO ASI Mining Massive Data Sets for Security (2007)
Gilles Blanchard, Olivier Bousquet, Laurent Zwald
Statistical properties of kernel principal component analysis. Mach. Learn. 66 (2-3) (2007)
Olivier Bousquet, André Elisseeff
Guest editorial: Learning theory. Mach. Learn. 66 (2-3) (2007)
Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet
Cascade Evaluation of Clustering Algorithms. ECML (2006)
Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet
SSC: Statistical Subspace Clustering. MLDM (2005)
Jason Weston, Bernhard Schölkopf, Olivier Bousquet
Joint Kernel Maps. IWANN (2005)
Matthias Hein, Olivier Bousquet, Bernhard Schölkopf
Maximal margin classification for metric spaces. J. Comput. Syst. Sci. 71 (3) (2005)
Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf
Evaluating Predictive Uncertainty Challenge. MLCW (2005)
Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf
Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT (2005)
Matthias Hein, Olivier Bousquet
Hilbertian Metrics and Positive Definite Kernels on Probability Measures. AISTATS (2005)
Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6 (2005)
Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet
SSC: statistical subspace clustering. EGC (2005)
Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis
Kernel Constrained Covariance for Dependence Measurement. AISTATS (2005)
Fernando Pérez-Cruz, Olivier Bousquet
Kernel methods and their potential use in signal processing. IEEE Signal Process. Mag. 21 (3) (2004)
Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf
A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. 5 (2004)
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT (2004)
Matthias Hein, Thomas Navin Lal, Olivier Bousquet
Hilbertian Metrics on Probability Measures and Their Application in SVM?s. DAGM-Symposium (2004)
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin
Limits of Spectral Clustering. NIPS (2004)
Laurent Zwald, Olivier Bousquet, Gilles Blanchard
Statistical Properties of Kernel Principal Component Analysis. COLT (2004)
Ulrike von Luxburg, Olivier Bousquet
Distance-Based Classification with Lipschitz Functions. J. Mach. Learn. Res. 5 (2004)
Jason Weston, Fernando Pérez-Cruz, Olivier Bousquet, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf
Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinform. 19 (6) (2003)
Olivier Bousquet, Fernando Pérez-Cruz
Kernel methods and their applications to signal processing. ICASSP (4) (2003)
Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi
Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning (2003)
Olivier Bousquet, Olivier Chapelle, Matthias Hein
Measure Based Regularization. NIPS (2003)
Matthias Hein, Olivier Bousquet
Maximal Margin Classification for Metric Spaces. COLT (2003)
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf
Ranking on Data Manifolds. NIPS (2003)
Jean-Yves Audibert, Olivier Bousquet
PAC-Bayesian Generic Chaining. NIPS (2003)
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf
Learning with Local and Global Consistency. NIPS (2003)
Ulrike von Luxburg, Olivier Bousquet
Distance-Based Classification with Lipschitz Functions. COLT (2003)
Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet
Concentration Inequalities. Advanced Lectures on Machine Learning (2003)
Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. COLT (2002)
Olivier Bousquet, Daniel J. L. Herrmann
On the Complexity of Learning the Kernel Matrix. NIPS (2002)
Olivier Bousquet, André Elisseeff
Stability and Generalization. J. Mach. Learn. Res. 2 (2002)
Olivier Bousquet, Manfred K. Warmuth
Tracking a Small Set of Experts by Mixing Past Posteriors. J. Mach. Learn. Res. 3 (2002)
Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. 46 (1-3) (2002)
Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson
Localized Rademacher Complexities. COLT (2002)
Olivier Bousquet, Manfred K. Warmuth
Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT (2001)
Olivier Bousquet, André Elisseeff
Algorithmic Stability and Generalization Performance. NIPS (2000)
Karthik Balakrishnan, Olivier Bousquet, Vasant G. Honavar
Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots. Adapt. Behav. 7 (2) (1999)