Olivier Bousquet

发表

Gradient Descent Quantizes ReLU Network Features pdf

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

Joint Kernel Maps

Bernhard Schölkopf, Jason Weston, Olivier Bousquet, 2005, IWANN.

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds

Dmitry Panchenko, Olivier Bousquet, Vladimir Koltchinskii, 2002, COLT.

Are GANs Created Equal? A Large-Scale Study

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

SSC: statistical subspace clustering

Isabelle Tellier, Fabien Torre, Olivier Bousquet, 2005, EGC.

PAC-Bayesian Generic Chaining

Jean-Yves Audibert, Olivier Bousquet, Jean-Yves Audibert, 2003, NIPS.

Distance-Based Classification with Lipschitz Functions

Ulrike von Luxburg, Olivier Bousquet, O. Bousquet, 2004, J. Mach. Learn. Res..

Passing Tests without Memorizing: Two Models for Fooling Discriminators pdf

Roi Livni, Shay Moran, Olivier Bousquet, 2019, ArXiv.

Kernel Constrained Covariance for Dependence Measurement

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005, AISTATS.

Statistical properties of kernel principal component analysis

Gilles Blanchard, Olivier Bousquet, Laurent Zwald, 2007, Machine Learning.

Advanced Statistical Learning Theory

Olivier Bousquet, O. Bousquet, 2004 .

Stability and Generalization

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2002, J. Mach. Learn. Res..

Ranking on Data Manifolds

Bernhard Schölkopf, Jason Weston, Arthur Gretton, 2003, NIPS.

A Multiple-Doppler Synthesis and Continuity Adjustment Technique (MUSCAT) to Recover Wind Components from Doppler Radar Measurements

Olivier Bousquet, Michel Chong, O. Bousquet, 1998 .

Measure Based Regularization

Matthias Hein, Olivier Chapelle, Olivier Bousquet, 2003, NIPS.

Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Matthias Hein, Olivier Bousquet, O. Bousquet, 2005, AISTATS.

Learning with Local and Global Consistency

Bernhard Schölkopf, Jason Weston, Dengyong Zhou, 2003, NIPS.

Maximal margin classification for metric spaces

Bernhard Schölkopf, Matthias Hein, Olivier Bousquet, 2005, J. Comput. Syst. Sci..

Toward Optimal Run Racing: Application to Deep Learning Calibration pdf

Michèle Sebag, Olivier Teytaud, Marc Schoenauer, 2017, ArXiv.

Advanced lectures on machine learning : ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003 : revised lectures

Gunnar Rätsch, Ulrike von Luxburg, Olivier Bousquet, 2004 .

Better Text Understanding Through Image-To-Text Transfer pdf

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

Kernel Methods for Measuring Independence

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005, J. Mach. Learn. Res..

Wasserstein Auto-Encoders pdf

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

Kernel-based dependence detection in the Macaque visual cortex

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005 .

Guest editorial: Learning theory

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2007, Machine Learning.

Evaluating Generative Models Using Divergence Frontiers pdf

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

Combining PAC-Bayesian and Generic Chaining Bounds

Jean-Yves Audibert, Olivier Bousquet, Jean-Yves Audibert, 2007, J. Mach. Learn. Res..

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

Vasant Honavar, Karthik Balakrishnan, Olivier Bousquet, 1999, Adapt. Behav..

AdaGAN: Boosting Generative Models

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

Statistical properties of Kernel Prinicipal Component Analysis

Gilles Blanchard, Olivier Bousquet, Laurent Zwald, 2019 .

Maximal Margin Classification for Metric Spaces

Matthias Hein, Olivier Bousquet, O. Bousquet, 2003, COLT.

Cascade Evaluation of Clustering Algorithms

Isabelle Tellier, Fabien Torre, Olivier Bousquet, 2006, ECML.

Tracking a Small Set of Experts by Mixing Past Posteriors

Manfred K. Warmuth, Olivier Bousquet, O. Bousquet, 2003, J. Mach. Learn. Res..

Kernel-based measures of dependence in the Macaque visual cortex

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005 .

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

Ulrike von Luxburg, Mikhail Belkin, Olivier Bousquet, 2004, COLT.

On the Complexity of Learning the Kernel Matrix

Olivier Bousquet, Daniel J. L. Herrmann, O. Bousquet, 2002, NIPS.

Assessing Generative Models via Precision and Recall

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

Critical Hyper-Parameters: No Random, No Cry pdf

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

A Compression Approach to Support Vector Model Selection

Bernhard Schölkopf, Ulrike von Luxburg, Olivier Bousquet, 2004, J. Mach. Learn. Res..

The Tradeoffs of Large Scale Learning

Léon Bottou, Olivier Bousquet, L. Bottou, 2007, NIPS.

Approximation and Convergence Properties of Generative Adversarial Learning

Kamalika Chaudhuri, Shuang Liu, Olivier Bousquet, 2017, NIPS.

Limits of Spectral Clustering

Ulrike von Luxburg, Mikhail Belkin, Olivier Bousquet, 2004, NIPS.

Learning using Large Datasets

Léon Bottou, Olivier Bousquet, L. Bottou, 2007, NATO ASI Mining Massive Data Sets for Security.

Algorithmic Stability and Generalization Performance

André Elisseeff, Olivier Bousquet, A. Elisseeff, 2000, NIPS.

Statistical Learning Theory

Olivier Bousquet, 2003 .

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

Matthias Hein, Thomas Navin Lal, Olivier Bousquet, 2004, DAGM-Symposium.

SuSE : Subspace Selection embedded in an EM algorithm

Isabelle Tellier, Fabien Torre, Olivier Bousquet, 2006 .

Online Hyper-Parameter Optimization

Nicolas Le Roux, Sylvain Gelly, Olivier Bousquet, 2018 .

Practical and Consistent Estimation of f-Divergences

Carlos Riquelme, Olivier Bousquet, Josip Djolonga, 2019, NeurIPS.

Introduction to Statistical Learning Theory

Gábor Lugosi, Stéphane Boucheron, Olivier Bousquet, 2004, Advanced Lectures on Machine Learning.

Localized Rademacher Complexities

Peter L. Bartlett, Shahar Mendelson, Olivier Bousquet, 2002, COLT.

Measuring Statistical Dependence with Hilbert-Schmidt Norms

Bernhard Schölkopf, Alexander J. Smola, Arthur Gretton, 2005, ALT.

Prototype Classification: Insights from Machine Learning

Gunnar Rätsch, Bernhard Schölkopf, Olivier Bousquet, 2009, Neural Computation.

Evaluating Predictive Uncertainty Challenge

Carl E. Rasmussen, Joaquin Quiñonero Candela, Bernhard Schölkopf, 2005, MLCW.

The Optimal Approximation Factor in Density Estimation pdf

Shay Moran, Olivier Bousquet, Daniel Kane, 2019, COLT.

Choosing Multiple Parameters for Support Vector Machines

Sayan Mukherjee, Olivier Chapelle, Vladimir Vapnik, 2002, Machine Learning.

Statistically Near-Optimal Hypothesis Selection pdf

Mark Braverman, Shay Moran, Klim Efremenko, 2021, ArXiv.

Semi-supervised Learning by Maximizing Smoothness

Jason Weston, Max Planck, Dengyong Zhou, 2004 .

Concentration Inequalities and Data-Dependent Error Bounds

Olivier Bousquet, O. Bousquet, 2003 .