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| Olivier Bousquet | |
|---|---|
| Name | Olivier Bousquet |
| Fields | Machine learning, Statistics |
| Workplaces | Google Research, INRIA, Télécom Paris |
| Alma mater | École Normale Supérieure, École Polytechnique |
| Doctoral advisor | Francis Bach |
Olivier Bousquet is a French researcher in machine learning, statistical learning theory, and kernel methods. He has held positions at Google Research, INRIA, and Télécom Paris, collaborating with researchers across France, United States, and United Kingdom. His work bridges theoretical aspects of support vector machines, generalization bounds, and practical applications in natural language processing, computer vision, and speech recognition.
Bousquet studied at École Normale Supérieure and École Polytechnique before completing doctoral work under Francis Bach at a French research institution affiliated with INRIA and Université Paris-Saclay. During his formative years he interacted with researchers from Massachusetts Institute of Technology, Stanford University, University of Cambridge, University of Oxford, and attended seminars alongside faculty from École Polytechnique Fédérale de Lausanne, Carnegie Mellon University, and University of California, Berkeley. His early academic network included collaborations with researchers from Google Research, Microsoft Research, Facebook AI Research, and participants at conferences such as NeurIPS, ICML, and COLT.
Bousquet contributed to theoretical foundations linking support vector machines, reproducing kernel Hilbert spaces, and uniform convergence methods exemplified at venues like NeurIPS, ICML, COLT, and AISTATS. He developed bounds related to Rademacher complexity and stability analyses that refined understanding of generalization error for algorithms used in computer vision, natural language processing, and speech recognition. His work intersects with ideas from Vladimir Vapnik, Corinna Cortes, Meir Feder, and influenced methods adopted by teams at Google Brain, DeepMind, and OpenAI. Collaborations incorporated techniques from convex optimization researchers linked to Yurii Nesterov, Stephen Boyd, and Aarti Singh for scalable kernel approximations and randomized feature methods inspired by Ali Rahimi and Ben Recht.
Bousquet held positions at INRIA and contributed to project teams involving Télécom Paris and ENS Paris. He later joined Google Research where he worked alongside scientists affiliated with Google Brain, DeepMind, and research groups that collaborate with University of Toronto, ETH Zurich, and University College London. He has served on program committees for NeurIPS, ICML, COLT, and advised doctoral students whose networks span University of California, Berkeley, Princeton University, and EPFL. His industry roles connected him with product teams collaborating with Apple, Microsoft, Amazon, and startups in the Silicon Valley ecosystem.
Bousquet received recognition from French and international institutions tied to INRIA, CNRS, and competitive awards presented at venues such as NeurIPS and ICML. Peers from École Normale Supérieure, École Polytechnique, and collaborators from Google Research have cited his theoretical contributions in surveys honoring researchers like Vladimir Vapnik, Yann LeCun, Geoffrey Hinton, and Andrew Ng. He has been invited to lecture at institutions including Princeton University, Harvard University, Massachusetts Institute of Technology, Stanford University, and University of Cambridge.
- "Stability and Generalization" — influential theoretical papers cited alongside works by Vladimir Vapnik, Corinna Cortes, and Meir Feder; presented at COLT and NeurIPS workshops attended by researchers from Google Brain and DeepMind. - Contributions to kernel methods and support vector machines comparing approaches from Yurii Nesterov-style optimization and randomized features from Ali Rahimi and Ben Recht; referenced in textbooks from MIT Press and course notes at École Polytechnique. - Reviews and tutorials on Rademacher complexity and algorithmic stability used in courses at University of Oxford, Carnegie Mellon University, and ETH Zurich.
Category:French computer scientists Category:Machine learning researchers