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Publications of Olivier Bousquet
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68 total
result as bibtex
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Proceedings (1)
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Bousquet, O., U. von Luxburg and G. Rätsch: Advanced Lectures on Machine Learning: ML Summer Schools 2003. ML Summer Schools 2003, 240, Springer, Berlin, Germany (09 2004)
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Journal Articles (21)
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Graf, A. B.A., O. Bousquet, G. Rätsch and B. Schölkopf: Prototype Classification: Insights from Machine Learning. Neural Computation 21(1), 272-300 (01 2009)
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von Luxburg, U., M. Belkin and O. Bousquet: Consistency of Spectral Clustering. Annals of Statistics 36(2), 555-586 (04 2008)
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Bousquet, O. and B. Schölkopf: Comment. Statistical Science 21(3), 337-340 (08 2006)
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Blanchard, G., O. Bousquet and L. Zwald: Statistical Properties of Kernel Principal Component Analysis. Machine Learning 66(2-3), 259-294 (03 2006)
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Gretton, A., R. Herbrich, A. Smola, O. Bousquet and B. Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6, 2075-2129 (12 2005)
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Hein, M., O. Bousquet and B. Schölkopf: Maximal Margin Classification for Metric Spaces. Journal of Computer and System Sciences 71(3), 333-359 (10 2005)
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Bartlett, P., O. Bousquet and S. Mendelson: Local Rademacher Complexities. The Annals of Statistics 33(4), 1497-1537 (08 2005)
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Boucheron, S., O. Bousquet and G. Lugosi: Theory of Classification: A Survey of Some Recent Advances. ESAIM: Probability and Statistics 9, 323 - 375 (2005)
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Boucheron, S., O. Bousquet, G. Lugosi and P. Massart: Moment Inequalities for Functions of Independent Random Variables. To appear in Annals of Probability 33, 514-560 (2005)
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von Luxburg, U. and O. Bousquet: Distance-Based Classification with Lipschitz Functions. Journal of Machine Learning Research 5, 669-695 (June 2004)
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von Luxburg, U., O. Bousquet and B. Schölkopf: A Compression Approach to Support Vector Model Selection. The Journal of Machine Learning Research 5, 293-323 (04 2004)
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Blanchard, G., O. Bousquet and P. Massart: Statistical Performance of Support Vector Machines. (submitted) (2004)
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Perez-Cruz, F. and O. Bousquet: Kernel Methods and their Potential Use in Signal Processing. IEEE Signal Processing Magazine (Special issue on Signal Processing for Mining) (in press) (2004)
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Bousquet, O.: Concentration Inequalities for Sub-Additive Functions Using the Entropy Method. Stochastic Inequalities and Applications 56, 213-247. (Eds.) Giné, E., C. Houdré and D. Nualart, Birkhauser (Nov 2003)
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Weston, J., F. Pérez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff and B. Schölkopf: Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics 19(6), 764-771 (04 2003)
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Bousquet, O.: New Approaches to Statistical Learning Theory. Annals of the Institute of Statistical Mathematics 55(2), 371-389 (2003)
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Bousquet, O.: A Bennett Concentration Inequality and Its Application to Suprema of Empirical Processes. C. R. Acad. Sci. Paris, Ser. I 334, 495-500 (2002)
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Bousquet, O. and A. Elisseeff: Stability and Generalization. Journal of Machine Learning Research 2, 499-526 (2002)
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Bousquet, O. and M. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. Journal of Machine Learning Research 3, 363-396. (Eds.) Long, P. MIT Press (2002)
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Chapelle, O., V. Vapnik, O. Bousquet and S. Mukherjee: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1), 131-159 (2002)
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Balakrishnan, K., O. Bousquet and V. Honavar: Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots. Adaptive Behavior 7(2), 173-216 (1999)
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Conference Papers (21)
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Quiñonero Candela, J., C. E. Rasmussen, F. Sinz, O. Bousquet and B. Schölkopf: Evaluating Predictive Uncertainty Challenge. Machine Learning Challenges: First PASCAL Machine Learning Challenges Workshop (MLCW 2005), 1-27. (Eds.) Quiñonero Candela, J., I. Dagan, B. Magnini, F. d’Alché-Buc, Springer, Berlin, Germany (04 2006)
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Gretton, A., O. Bousquet, A. Smola and B. Schoelkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. Algorithmic Learning Theory: 16th International Conference, ALT 2005, 63-78 (10/08/ 2005)
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von Luxburg, U., O. Bousquet and M. Belkin: Limits of Spectral Clustering. Advances in Neural Information Processing Systems 17: Proceedings of the 2004 Conference, 857-864. (Eds.) Saul, L. K., Y. Weiss, L. Bottou, MIT Press, Cambridge, MA, USA (07 2005)
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Gretton, A., A. J. Smola, O. Bousquet, R. Herbrich, A. Belitski, M. Augath, Y. Murayama, J. Pauls, B. Schölkopf and N. K. Logothetis: Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005 10, 1-8 (01 2005)
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Hein, M. and O. Bousquet: Hilbertian metrics and positive definite kernels on probability measures. Proceedings of AISTATS 2005, 136-143. (Eds.) Ghahramani, Z., R. Cowell (01 2005)
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Weston, J., B. Schölkopf and O. Bousquet: Joint Kernel Maps. Proceedings of the 8th International Work-Conference on Artificial Neural Networks (Computational Intelligence and Bioinspired System) LNCS 3512, 176-191. (Eds.) Cabestany, J., A. Prieto, F. Sandoval, Springer-Verlag, Berlin Heidelberg, Germany (2005)
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Bousquet, O., Chapelle, O. and Hein, M.: Measure Based Regularization. Advances in Neural Information Processing Systems 16. (Eds.) Thrun, S., L. Saul and B. Schölkopf, MIT Press, Cambridge, MA USA (December 2004)
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Hein, M. and O. Bousquet: Maximal Margin Classification for Metric Spaces. Learning Theory and Kernel Machines, 72-86. (Eds.) Schölkopf, B., M. K. Warmuth, Springer Verlag, Heidelberg, Germany (02 2004)
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Audibert, J. -Y. and O. Bousquet: PAC-Bayesian Generic Chaining. Advances in Neural Information Processing Systems 16, 1125 - 1132. (Eds.) Thrun, S., L. Saul, B. Schölkopf, The MIT Press, Cambridge, MA, USA (2004)
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Hein, H., T.N. Lal and O. Bousquet: Hilbertian Metrics on Probability Measures and their Application in SVM's. Pattern Recognition, Proceedings of th 26th DAGM Symposium 3175, 270-277. (Eds.) Rasmussen, C. E., H. H. Bülthoff, M. Giese and B. Schölkopf, Springer, Berlin, Germany (2004)
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von Luxburg, U., O. Bousquet and M. Belkin: On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. Proceedings of the 17th Annual Conference on Learning Theory, 457-471, Springer, Berlin (2004)
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Zhou, D., J. Weston, A. Gretton, O. Bousquet and B. Schölkopf: Ranking on Data Manifolds. Advances in Neural Information Processing Systems 16, 169-176. (Eds.) Thrun, S., L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, USA (2004)
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Zhou, D., O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf: Learning with Local and Global Consistency. Advances in Neural Information Processing Systems 16, 321-328. (Eds.) Thrun, S., L. Saul and B. Schölkopf, MIT Press, Cambridge, MA, USA (2004)
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Bousquet, O. and D. Herrmann: On the Complexity of Learning the Kernel Matrix. Advances in Neural Information Processing Systems, 15, 415-422, The MIT Press, Cambridge, MA, USA (2003)
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Bousquet, O. and F. Perez-Cruz: Kernel Methods and Their Applications to Signal Processing. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03) Special Session on Kernel Methods, 860 - 863 (2003)
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von Luxburg, U. and O. Bousquet: Distance-based classification with Lipschitz functions. Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 314-328. (Eds.) Schölkopf, B. and M.K. Warmuth, Springer Verlag, Berlin - Heidelberg, Germany (2003)
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Bartlett, P., O. Bousquet and S. Mendelson: Localized Rademacher Complexities. Proceedings of the 15th annual conference on Computational Learning Theory, 44-58 (2002)
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Bousquet, O., V. Koltchinskii and D. Panchenko: Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. Proceedings of the 15th annual conference on Computational Learning Theory (2002)
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Bousquet, O. and A. Elisseeff: Algorithmic Stability and Generalization Performance. Advances in Neural Information Processing Systems 13, 196-202, MIT Press (2001)
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Bousquet, O. and M. Warmuth: Tracking a Small Set of Experts by Mixing Past Posteriors. Proceedings of the 14th Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science 2111, 31-47, Springer (2001)
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Bousquet, O., K. Balakrishnan and V. Honavar: Is the Hippocampus a Kalman Filter? Proceedings of the Pacific Symposium on Biocomputing 3, 619-630 (1999)
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Book Chapters (2)
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Boucheron, S., G. Lugosi and O. Bousquet: Concentration Inequalities. Advanced Lectures on Machine Learning Lecture Notes in Artificial Intelligence 3176, 208-240. (Eds.) Bousquet, O., U. von Luxburg and G. Rätsch, Springer, Heidelberg, Germany (2004)
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Bousquet, O., S. Boucheron and G. Lugosi: Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning Lecture Notes in Artificial Intelligence 3176, 169-207. (Eds.) Bousquet, O., U. von Luxburg and G. Rätsch, Springer, Heidelberg, Germany (2004)
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MPI-Technical Reports (9)
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Gretton, A., O. Bousquet, A. J. Smola and B. Schölkopf: Measuring Statistical Dependence with Hilbert-Schmidt Norms. MPI Technical Report (140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (06 2005)
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M. Hein and O. Bousquet: Hilbertian Metrics and Positive Definite Kernels on Probability Measures. (126) (July 2004)
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M. Hein and O. Bousquet: Kernels, Associated Structures and Generalizations. (127) (July 2004)
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von Luxburg, U., Mikhail Belkin and O. Bousquet: Consistency of Spectral Clustering. (134), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (December 2004)
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Weston, J., B. Schölkopf, O. Bousquet, Mann and Noble: Joint Kernel Maps. (131) (11 2004)
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Gretton, A., A. Smola, O. Bousquet, R. Herbrich, B. Schölkopf and N.K. Logothetis: Behaviour and Convergence of the Constrained Covariance. MPI Technical Report (130), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2004)
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Zhou, D., J. Weston, A. Gretton, O. Bousquet and B. Schölkopf: Ranking on Data Manifolds. MPI Technical Report (113), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (June 2003)
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Zhou, D., O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf: Learning with Local and Global Consistency. MPI Technical Report (112), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (June 2003)
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von Luxburg, U., O. Bousquet and B. Schölkopf: A compression approach to support vector model selection. (101), Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2002) [Note: see more detailed JMLR version]
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Technical Reports (3)
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Bousquet, O.: A Note on Parameter Tuning for On-Line Shifting Algorithms. Technical report (2003)
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Weston, J., F. Pérez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff and B. Schölkopf: Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design. Technical report (2002)
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Weston, J., F. Perez-Cruz, O. Bousquet, O. Chapelle, A. Elisseeff and B. Schölkopf: KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug design -- Binding to Thrombin. Technical report (2001)
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PhD Theses (1)
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Bousquet, O.: Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms. (in press) (2002)
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Diploma Theses (1)
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Bousquet, O.: Apprentissage Automatique et Simplicite. (1999) [Note: In french]
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Talks (9)
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Gretton, A., A. Smola, O. Bousquet, R. Herbrich, A. Belitski, M. Augath, Y. Murayama, B. Schölkopf and N. K. Logothetis: Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005 (01 2005)
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Bousquet, O.: Introduction to Category Theory. Internal Seminar (Jan 2004)
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Bousquet, O.: Advanced Statistical Learning Theory. Machine Learning Summer School 2004 (2004)
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Bousquet, O.: Rademacher and Gaussian averages in Learning Theory. Universite de Marne-la-Vallee (Mar 2003)
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Bousquet, O. and B. Schölkopf: Statistical Learning Theory. (Mar 2003)
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Bousquet, O.: Concentration Inequalities and Data-Dependent Error Bounds. Uni. Jena (Feb 2003)
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Bousquet, O.: Statistical Learning Theory. Machine Learning Summer School 2003, Tuebingen, Germany (Aug 2003)
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Bousquet, O.: Remarks on Statistical Learning Theory. Machine Learning Summer School 2003, Tuebingen, Germany (Aug 2003)
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Bousquet, O.: Transductive Learning: Motivation, Models, Algorithms. (Jan 2002)
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