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Semantic ScholarOlivier Bousquet
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- affiliation: Google Switzerland, Zurich
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2010 – today
- 2018
[c31]Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. NeurIPS 2018: 698-707- [c30]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. NeurIPS 2018: 5234-5243 - [i9]Hartmut Maennel, Olivier Bousquet, Sylvain Gelly:
Gradient Descent Quantizes ReLU Network Features. CoRR abs/1803.08367 (2018) - [i8]Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly:
Assessing Generative Models via Precision and Recall. CoRR abs/1806.00035 (2018) - 2017
- [c29]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. NIPS 2017: 5430-5439 - [c28]Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. NIPS 2017: 5551-5559 - [i7]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. CoRR abs/1701.02386 (2017) - [i6]Karol Kurach, Sylvain Gelly, Michal Jastrzebski, Philip Häusser, Olivier Teytaud, Damien Vincent, Olivier Bousquet:
Better Text Understanding Through Image-To-Text Transfer. CoRR abs/1705.08386 (2017) - [i5]Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. CoRR abs/1705.08991 (2017) - [i4]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent:
Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR abs/1706.03199 (2017) - [i3]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Olivier Teytaud, Damien Vincent:
Critical Hyper-Parameters: No Random, No Cry. CoRR abs/1706.03200 (2017) - [i2]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. CoRR abs/1711.01558 (2017) - [i1]Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. CoRR abs/1711.10337 (2017)
2000 – 2009
- 2009
- [j13]Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf:
Prototype Classification: Insights from Machine Learning. Neural Computation 21(1): 272-300 (2009) - 2007
- [j12]Jean-Yves Audibert, Olivier Bousquet:
Combining PAC-Bayesian and Generic Chaining Bounds. Journal of Machine Learning Research 8: 863-889 (2007) - [j11]Olivier Bousquet, André Elisseeff:
Guest editorial: Learning theory. Machine Learning 66(2-3): 115-118 (2007) - [j10]Gilles Blanchard, Olivier Bousquet, Laurent Zwald:
Statistical properties of kernel principal component analysis. Machine Learning 66(2-3): 259-294 (2007) - [c27]
- 2006
- [c26]Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet:
Cascade Evaluation of Clustering Algorithms. ECML 2006: 574-581 - 2005
- [j9]Matthias Hein, Olivier Bousquet, Bernhard Schölkopf:
Maximal margin classification for metric spaces. J. Comput. Syst. Sci. 71(3): 333-359 (2005) - [j8]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) - [c25]Matthias Hein, Olivier Bousquet:
Hilbertian Metrics and Positive Definite Kernels on Probability Measures. AISTATS 2005 - [c24]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 - [c23]Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf:
Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77 - [c22]Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet:
SSC: statistical subspace clustering. EGC 2005: 177-182 - [c21]
- [c20]Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf:
Evaluating Predictive Uncertainty Challenge. MLCW 2005: 1-27 - [c19]Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet:
SSC: Statistical Subspace Clustering. MLDM 2005: 100-109 - 2004
- [j7]Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf:
A Compression Approach to Support Vector Model Selection. Journal of Machine Learning Research 5: 293-323 (2004) - [j6]Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. Journal of Machine Learning Research 5: 669-695 (2004) - [c18]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
On the Convergence of Spectral Clustering on Random Samples: The Normalized Case. COLT 2004: 457-471 - [c17]Laurent Zwald, Olivier Bousquet, Gilles Blanchard:
Statistical Properties of Kernel Principal Component Analysis. COLT 2004: 594-608 - [c16]Matthias Hein, Thomas Navin Lal, Olivier Bousquet:
Hilbertian Metrics on Probability Measures and Their Application in SVM?s. DAGM-Symposium 2004: 270-277 - [c15]Ulrike von Luxburg, Olivier Bousquet, Mikhail Belkin:
Limits of Spectral Clustering. NIPS 2004: 857-864 - [e1]Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch:
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 [contents] - 2003
- [j5]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) - [c14]Olivier Bousquet, Stéphane Boucheron, Gábor Lugosi:
Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning 2003: 169-207 - [c13]Stéphane Boucheron, Gábor Lugosi, Olivier Bousquet:
Concentration Inequalities. Advanced Lectures on Machine Learning 2003: 208-240 - [c12]
- [c11]Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. COLT 2003: 314-328 - [c10]Olivier Bousquet, Fernando Pérez-Cruz:
Kernel methods and their applications to signal processing. ICASSP (4) 2003: 860-863 - [c9]Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf:
Ranking on Data Manifolds. NIPS 2003: 169-176 - [c8]Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf:
Learning with Local and Global Consistency. NIPS 2003: 321-328 - [c7]
- [c6]Olivier Bousquet, Olivier Chapelle, Matthias Hein:
Measure Based Regularization. NIPS 2003: 1221-1228 - 2002
- [j4]Olivier Bousquet, André Elisseeff:
Stability and Generalization. Journal of Machine Learning Research 2: 499-526 (2002) - [j3]Olivier Bousquet, Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors. Journal of Machine Learning Research 3: 363-396 (2002) - [j2]Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee:
Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1-3): 131-159 (2002) - [c5]Peter L. Bartlett, Olivier Bousquet, Shahar Mendelson:
Localized Rademacher Complexities. COLT 2002: 44-58 - [c4]Olivier Bousquet, Vladimir Koltchinskii, Dmitriy Panchenko:
Some Local Measures of Complexity of Convex Hulls and Generalization Bounds. COLT 2002: 59-73 - [c3]Olivier Bousquet, Daniel J. L. Herrmann:
On the Complexity of Learning the Kernel Matrix. NIPS 2002: 399-406 - 2001
- [c2]Olivier Bousquet, Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors. COLT/EuroCOLT 2001: 31-47 - 2000
- [c1]Olivier Bousquet, André Elisseeff:
Algorithmic Stability and Generalization Performance. NIPS 2000: 196-202
1990 – 1999
- 1999
- [j1]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. Adaptive Behaviour 7(2): 173-216 (1999)
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