default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDOlivier Bousquet
Person information
- affiliation: Google Switzerland, Zurich
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j16]Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. J. Mach. Learn. Res. 24: 316:1-316:36 (2023)- [c49]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. COLT 2023: 5890-5924 - [c48]Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou:
Compositional Semantic Parsing with Large Language Models. ICLR 2023 - [c47]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ICLR 2023 - [i32]Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran:
Diagonalization Games. CoRR abs/2301.01924 (2023) - [i31]Noga Alon, Olivier Bousquet, Kasper Green Larsen, Shay Moran, Shlomo Moran:
Diagonalization Games. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [c46]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. COLT 2022: 842-866 - [i30]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. CoRR abs/2202.05246 (2022) - [i29]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. CoRR abs/2205.10625 (2022) - [i28]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. CoRR abs/2208.14615 (2022) - [i27]Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou:
Compositional Semantic Parsing with Large Language Models. CoRR abs/2209.15003 (2022) - [i26]Peter L. Bartlett, Philip M. Long, Olivier Bousquet:
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima. CoRR abs/2210.01513 (2022) - [i25]Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer:
Differentially-Private Bayes Consistency. CoRR abs/2212.04216 (2022) - 2021
- [c45]Olivier Bousquet, Mark Braverman, Gillat Kol, Klim Efremenko
, Shay Moran:
Statistically Near-Optimal Hypothesis Selection. FOCS 2021: 909-919 - [c44]Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff:
A theory of universal learning. STOC 2021: 532-541 - [i24]Olivier Bousquet, Mark Braverman, Klim Efremenko, Gillat Kol, Shay Moran:
Statistically Near-Optimal Hypothesis Selection. CoRR abs/2108.07880 (2021) - 2020
- [c43]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. AAAI 2020: 4501-4510 - [c42]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS 2020: 2550-2559 - [c41]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. COLT 2020: 582-609 - [c40]Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy:
Sharper Bounds for Uniformly Stable Algorithms. COLT 2020: 610-626 - [c39]Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet:
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. ICLR 2020 - [c38]Olivier Bousquet, Roi Livni, Shay Moran:
Synthetic Data Generators - Sequential and Private. NeurIPS 2020 - [c37]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? NeurIPS 2020 - [i23]Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin:
Predicting Neural Network Accuracy from Weights. CoRR abs/2002.11448 (2020) - [i22]Olivier Bousquet, Steve Hanneke, Shay Moran, Nikita Zhivotovskiy:
Proper Learning, Helly Number, and an Optimal SVM Bound. CoRR abs/2005.11818 (2020) - [i21]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? CoRR abs/2006.10455 (2020) - [i20]Olivier Bousquet, Steve Hanneke, Shay Moran, Ramon van Handel, Amir Yehudayoff:
A Theory of Universal Learning. CoRR abs/2011.04483 (2020)
2010 – 2019
- 2019
- [c36]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. COLT 2019: 318-341 - [c35]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? COLT 2019: 1500-1518 - [c34]Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. NeurIPS 2019: 4072-4082 - [i19]Olivier Bousquet, Roi Livni, Shay Moran:
Passing Tests without Memorizing: Two Models for Fooling Discriminators. CoRR abs/1902.03468 (2019) - [i18]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. CoRR abs/1902.05876 (2019) - [i17]Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly:
Evaluating Generative Models Using Divergence Frontiers. CoRR abs/1905.10768 (2019) - [i16]Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. CoRR abs/1905.11112 (2019) - [i15]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? CoRR abs/1905.11866 (2019) - [i14]Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly:
Google Research Football: A Novel Reinforcement Learning Environment. CoRR abs/1907.11180 (2019) - [i13]Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby:
The Visual Task Adaptation Benchmark. CoRR abs/1910.04867 (2019) - [i12]Olivier Bousquet, Yegor Klochkov, Nikita Zhivotovskiy:
Sharper bounds for uniformly stable algorithms. CoRR abs/1910.07833 (2019) - [i11]Olivier Bousquet, Nikita Zhivotovskiy:
Fast classification rates without standard margin assumptions. CoRR abs/1910.12756 (2019) - [i10]Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, Olivier Bousquet:
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. CoRR abs/1912.09713 (2019) - 2018
- [c33]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. ICLR 2018 - [c32]Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet:
Are GANs Created Equal? A Large-Scale Study. NeurIPS 2018: 698-707 - [c31]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
- [c30]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. NIPS 2017: 5424-5433 - [c29]Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri:
Approximation and Convergence Properties of Generative Adversarial Learning. NIPS 2017: 5545-5553 - [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) - 2010
- [j15]Léon Bottou, Olivier Bousquet:
L'apprentissage statistique à grande échelle. Monde des Util. Anal. Données 42: 61-73 (2010)
2000 – 2009
- 2009
- [j14]Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf:
Prototype Classification: Insights from Machine Learning. Neural Comput. 21(1): 272-300 (2009) - 2007
- [j13]Jean-Yves Audibert, Olivier Bousquet:
Combining PAC-Bayesian and Generic Chaining Bounds. J. Mach. Learn. Res. 8: 863-889 (2007) - [j12]Olivier Bousquet, André Elisseeff:
Guest editorial: Learning theory. Mach. Learn. 66(2-3): 115-118 (2007) - [j11]Gilles Blanchard, Olivier Bousquet, Laurent Zwald:
Statistical properties of kernel principal component analysis. Mach. Learn. 66(2-3): 259-294 (2007) - [c28]Léon Bottou, Olivier Bousquet:
Learning using Large Datasets. NATO ASI Mining Massive Data Sets for Security 2007: 15-26 - [c27]Léon Bottou, Olivier Bousquet:
The Tradeoffs of Large Scale Learning. NIPS 2007: 161-168 - 2006
- [c26]Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet:
Cascade Evaluation of Clustering Algorithms. ECML 2006: 574-581 - 2005
- [j10]Matthias Hein, Olivier Bousquet, Bernhard Schölkopf:
Maximal margin classification for metric spaces. J. Comput. Syst. Sci. 71(3): 333-359 (2005) - [j9]Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6: 2075-2129 (2005) - [c25]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: 112-119 - [c24]Matthias Hein, Olivier Bousquet:
Hilbertian Metrics and Positive Definite Kernels on Probability Measures. AISTATS 2005: 136-143 - [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]Jason Weston, Bernhard Schölkopf, Olivier Bousquet:
Joint Kernel Maps. IWANN 2005: 176-191 - [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
- [j8]Ulrike von Luxburg, Olivier Bousquet, Bernhard Schölkopf:
A Compression Approach to Support Vector Model Selection. J. Mach. Learn. Res. 5: 293-323 (2004) - [j7]Ulrike von Luxburg, Olivier Bousquet:
Distance-Based Classification with Lipschitz Functions. J. Mach. Learn. Res. 5: 669-695 (2004) - [j6]Fernando Pérez-Cruz, Olivier Bousquet:
Kernel methods and their potential use in signal processing. IEEE Signal Process. Mag. 21(3): 57-65 (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. Bioinform. 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]Matthias Hein, Olivier Bousquet:
Maximal Margin Classification for Metric Spaces. COLT 2003: 72-86 - [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]Jean-Yves Audibert, Olivier Bousquet:
PAC-Bayesian Generic Chaining. NIPS 2003: 1125-1132 - [c6]Olivier Bousquet, Olivier Chapelle, Matthias Hein:
Measure Based Regularization. NIPS 2003: 1221-1228 - 2002
- [j4]Olivier Bousquet, André Elisseeff:
Stability and Generalization. J. Mach. Learn. Res. 2: 499-526 (2002) - [j3]Olivier Bousquet, Manfred K. Warmuth:
Tracking a Small Set of Experts by Mixing Past Posteriors. J. Mach. Learn. Res. 3: 363-396 (2002) - [j2]Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee:
Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. 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. Adapt. Behav. 7(2): 173-216 (1999)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-07 21:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
