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)

Algorithmic Stability and Generalization Performance. - Olivier Bousquet; André Elisseeff (NIPS, 2000)

Localized Rademacher Complexities. - Peter L. Bartlett; Olivier Bousquet; Shahar Mendelson (COLT, 2002)

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. - Olivier Bousquet; Vladimir Koltchinskii; Dmitriy Panchenko (COLT, 2002)

On the Complexity of Learning the Kernel Matrix. - Olivier Bousquet; Daniel J. L. Herrmann (NIPS, 2002)

Stability and Generalization. - Olivier Bousquet; André Elisseeff (J. Mach. Learn. Res., 2002)

Tracking a Small Set of Experts by Mixing Past Posteriors. - Olivier Bousquet; Manfred K. Warmuth (J. Mach. Learn. Res., 2002)

Choosing Multiple Parameters for Support Vector Machines. - Olivier Chapelle; Vladimir Vapnik; Olivier Bousquet; Sayan Mukherjee 001 (Mach. Learn., 2002)

Concentration Inequalities. - Stéphane Boucheron; Gábor Lugosi; Olivier Bousquet (Advanced Lectures on Machine Learning, 2003)

Introduction to Statistical Learning Theory. - Olivier Bousquet; Stéphane Boucheron; Gábor Lugosi (Advanced Lectures on Machine Learning, 2003)

Maximal Margin Classification for Metric Spaces. - Matthias Hein 001; Olivier Bousquet (COLT, 2003)

Kernel methods and their applications to signal processing. - Olivier Bousquet; Fernando Pérez-Cruz (ICASSP (4), 2003)

PAC-Bayesian Generic Chaining. - Jean-Yves Audibert; Olivier Bousquet (NIPS, 2003)

Measure Based Regularization. - Olivier Bousquet; Olivier Chapelle; Matthias Hein 001 (NIPS, 2003)

Learning with Local and Global Consistency. - Dengyong Zhou; Olivier Bousquet; Thomas Navin Lal; Jason Weston; Bernhard Schölkopf (NIPS, 2003)

Ranking on Data Manifolds. - Dengyong Zhou; Jason Weston; Arthur Gretton; Olivier Bousquet; Bernhard Schölkopf (NIPS, 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)

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (COLT, 2004)

Hilbertian Metrics on Probability Measures and Their Application in SVM?s. - Matthias Hein 001; Thomas Navin Lal; Olivier Bousquet (DAGM-Symposium, 2004)

Limits of Spectral Clustering. - Ulrike von Luxburg; Olivier Bousquet; Mikhail Belkin (NIPS, 2004)

Distance-Based Classification with Lipschitz Functions. - Ulrike von Luxburg; Olivier Bousquet (J. Mach. Learn. Res., 2004)

A Compression Approach to Support Vector Model Selection. - Ulrike von Luxburg; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2004)

Kernel methods and their potential use in signal processing. - Fernando Pérez-Cruz; Olivier Bousquet (IEEE Signal Process. Mag., 2004)

Hilbertian Metrics and Positive Definite Kernels on Probability Measures. - Matthias Hein 001; Olivier Bousquet (AISTATS, 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 (AISTATS, 2005)

Measuring Statistical Dependence with Hilbert-Schmidt Norms. - Arthur Gretton; Olivier Bousquet; Alexander J. Smola; Bernhard Schölkopf (ALT, 2005)

Joint Kernel Maps. - Jason Weston; Bernhard Schölkopf; Olivier Bousquet (IWANN, 2005)

Evaluating Predictive Uncertainty Challenge. - Joaquin Quiñonero Candela; Carl Edward Rasmussen; Fabian H. Sinz; Olivier Bousquet; Bernhard Schölkopf (MLCW, 2005)

SSC: Statistical Subspace Clustering. - Laurent Candillier; Isabelle Tellier; Fabien Torre; Olivier Bousquet (MLDM, 2005)

Maximal margin classification for metric spaces. - Matthias Hein 001; Olivier Bousquet; Bernhard Schölkopf (J. Comput. Syst. Sci., 2005)

Kernel Methods for Measuring Independence. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2005)

Cascade Evaluation of Clustering Algorithms. - Laurent Candillier; Isabelle Tellier; Fabien Torre; Olivier Bousquet (ECML, 2006)

Learning using Large Datasets. - Léon Bottou; Olivier Bousquet (NATO ASI Mining Massive Data Sets for Security, 2007)

The Tradeoffs of Large Scale Learning. - Léon Bottou; Olivier Bousquet (NIPS, 2007)

Combining PAC-Bayesian and Generic Chaining Bounds. - Jean-Yves Audibert; Olivier Bousquet (J. Mach. Learn. Res., 2007)

Statistical properties of kernel principal component analysis. - Gilles Blanchard; Olivier Bousquet; Laurent Zwald (Mach. Learn., 2007)

Guest editorial: Learning theory. - Olivier Bousquet; André Elisseeff (Mach. Learn., 2007)

Prototype Classification: Insights from Machine Learning. - Arnulf B. A. Graf; Olivier Bousquet; Gunnar Rätsch; Bernhard Schölkopf (Neural Comput., 2009)

L'apprentissage statistique à grande échelle. - Léon Bottou; Olivier Bousquet (Monde des Util. Anal. Données, 2010)

Approximation and Convergence Properties of Generative Adversarial Learning. - Shuang Liu; Olivier Bousquet; Kamalika Chaudhuri (NIPS, 2017)

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)

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)

The Optimal Approximation Factor in Density Estimation. - Olivier Bousquet; Daniel Kane 001; Shay Moran (COLT, 2019)

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

Practical and Consistent Estimation of f-Divergences. - Paul K. Rubenstein; Olivier Bousquet; Josip Djolonga; Carlos Riquelme; Ilya O. Tolstikhin (NeurIPS, 2019)

Passing Tests without Memorizing: Two Models for Fooling Discriminators. - Olivier Bousquet; Roi Livni; Shay Moran (CoRR, 2019)

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

Fast classification rates without standard margin assumptions. - Olivier Bousquet; Nikita Zhivotovskiy (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)

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

Proper Learning, Helly Number, and an Optimal SVM Bound. - Olivier Bousquet; Steve Hanneke; Shay Moran; Nikita Zhivotovskiy (COLT, 2020)

Sharper Bounds for Uniformly Stable Algorithms. - Olivier Bousquet; Yegor Klochkov; Nikita Zhivotovskiy (COLT, 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 038; et al. (ICLR, 2020)

Synthetic Data Generators - Sequential and Private. - Olivier Bousquet; Roi Livni; Shay Moran (NeurIPS, 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)

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

Statistically Near-Optimal Hypothesis Selection. - Olivier Bousquet; Mark Braverman; Gillat Kol; Klim Efremenko; Shay Moran (FOCS, 2021)

A theory of universal learning. - Olivier Bousquet; Steve Hanneke; Shay Moran; Ramon van Handel; Amir Yehudayoff (STOC, 2021)

Monotone Learning. - Olivier Bousquet; Amit Daniely; Haim Kaplan; Yishay Mansour; Shay Moran; Uri Stemmer (COLT, 2022)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Olivier Bousquet; Quoc Le 001; Ed H. Chi (CoRR, 2022)

Differentially-Private Bayes Consistency. - Olivier Bousquet; Haim Kaplan; Aryeh Kontorovich; Yishay Mansour; Shay Moran; Menachem Sadigurschi; Uri Stemmer (CoRR, 2022)

Fine-Grained Distribution-Dependent Learning Curves. - Olivier Bousquet; Steve Hanneke; Shay Moran; Jonathan Shafer; Ilya O. Tolstikhin (COLT, 2023)

Compositional Semantic Parsing with Large Language Models. - Andrew Drozdov; Nathanael Schärli; Ekin Akyürek; Nathan Scales; Xinying Song; Xinyun Chen; Olivier Bousquet; Denny Zhou (ICLR, 2023)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. - Denny Zhou; Nathanael Schärli; Le Hou; Jason Wei; Nathan Scales; Xuezhi Wang 002; Dale Schuurmans; Claire Cui; Olivier Bousquet; Quoc V. Le; Ed H. Chi (ICLR, 2023)

The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. - Peter L. Bartlett; Philip M. Long; Olivier Bousquet (J. Mach. Learn. Res., 2023)

Diagonalization Games. - Noga Alon; Olivier Bousquet; Kasper Green Larsen; Shay Moran; Shlomo Moran (Am. Math. Mon., 2024)