Olivier Bousquet

Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf: Prototype Classification: Insights from Machine Learning. Neural Computation 21(1): 272-300 (2009)

Jean-Yves Audibert, Olivier Bousquet: Combining PAC-Bayesian and Generic Chaining Bounds. Journal of Machine Learning Research 8: 863-889 (2007)

Olivier Bousquet, André Elisseeff: Guest editorial: Learning theory. Machine Learning 66(2-3): 115-118 (2007)

Gilles Blanchard, Olivier Bousquet, Laurent Zwald: Statistical properties of kernel principal component analysis. Machine Learning 66(2-3): 259-294 (2007)

Léon Bottou, Olivier Bousquet: The Tradeoffs of Large Scale Learning. NIPS 2007

Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet: Cascade Evaluation of Clustering Algorithms. ECML 2006: 574-581

Matthias Hein, Olivier Bousquet, Bernhard Schölkopf: Maximal margin classification for metric spaces. J. Comput. Syst. Sci. 71(3): 333-359 (2005)

Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6: 2075-2129 (2005)

Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77

Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet: SSC: statistical subspace clustering. EGC 2005: 177-182

Jason Weston, Bernhard Schölkopf, Olivier Bousquet: Joint Kernel Maps. IWANN 2005: 176-191

Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf: Evaluating Predictive Uncertainty Challenge. MLCW 2005: 1-27

Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet: SSC: Statistical Subspace Clustering. MLDM 2005: 100-109

Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf: A Compression Approach to Support Vector Model Selection. Journal of Machine Learning Research 5: 293-323 (2004)

Ulrike von Luxburg, Olivier Bousquet: Distance-Based Classification with Lipschitz Functions. Journal of Machine Learning Research 5: 669-695 (2004)

Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin: On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT 2004: 457-471

Laurent Zwald, Olivier Bousquet, Gilles Blanchard: Statistical Properties of Kernel Principal Component Analysis. COLT 2004: 594-608

Matthias Hein, Thomas Navin Lal, Olivier Bousquet: Hilbertian Metrics on Probability Measures and Their Application in SVM?s. DAGM-Symposium 2004: 270-277

Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin: Limits of Spectral Clustering. NIPS 2004

Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch (Eds.): Advanced Lectures on Machine Learning, ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures. Lecture Notes in Computer Science 3176, Springer 2004, isbn 3-540-23122-6

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. Bioinformatics 19(6): 764-771 (2003)

Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi: Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning 2003: 169-207

Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet: Concentration Inequalities. Advanced Lectures on Machine Learning 2003: 208-240

Matthias Hein, Olivier Bousquet: Maximal Margin Classification for Metric Spaces. COLT 2003: 72-86

Ulrike von Luxburg, Olivier Bousquet: Distance-Based Classification with Lipschitz Functions. COLT 2003: 314-328

Jean-Yves Audibert, Olivier Bousquet: PAC-Bayesian Generic Chaining. NIPS 2003

Olivier Bousquet, Olivier Chapelle, Matthias Hein: Measure Based Regularization. NIPS 2003

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

Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf: Ranking on Data Manifolds. NIPS 2003

Olivier Bousquet, André Elisseeff: Stability and Generalization. Journal of Machine Learning Research 2: 499-526 (2002)

Olivier Bousquet, André Elisseeff: Stability and Generalization. Journal of Machine Learning Research 2: 499-526 (2002)

Olivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. Journal of Machine Learning Research 3: 363-396 (2002)

Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1-3): 131-159 (2002)

Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson: Localized Rademacher Complexities. COLT 2002: 44-58

Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko: Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. COLT 2002: 59-73

Olivier Bousquet, Daniel J. L. Herrmann: On the Complexity of Learning the Kernel Matrix. NIPS 2002: 399-406

Olivier Bousquet, Manfred K. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT 2001: 31-47

Olivier Bousquet, André Elisseeff: Algorithmic Stability and Generalization Performance. NIPS 2000: 196-202