Olivier Bousquet

Affiliations:
  • Google Switzerland, Zurich


According to our database1, Olivier Bousquet authored at least 65 papers between 1999 and 2021.

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Bibliography

2021
Statistically Near-Optimal Hypothesis Selection.
CoRR, 2021

A theory of universal learning.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

2020
Predicting Neural Network Accuracy from Weights.
CoRR, 2020

What Do Neural Networks Learn When Trained With Random Labels?
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Synthetic Data Generators - Sequential and Private.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data.
Proceedings of the 8th International Conference on Learning Representations, 2020

Sharper Bounds for Uniformly Stable Algorithms.
Proceedings of the Conference on Learning Theory, 2020

Proper Learning, Helly Number, and an Optimal SVM Bound.
Proceedings of the Conference on Learning Theory, 2020

Precision-Recall Curves Using Information Divergence Frontiers.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Google Research Football: A Novel Reinforcement Learning Environment.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Fast classification rates without standard margin assumptions.
CoRR, 2019

The Visual Task Adaptation Benchmark.
CoRR, 2019

Evaluating Generative Models Using Divergence Frontiers.
CoRR, 2019

Passing Tests without Memorizing: Two Models for Fooling Discriminators.
CoRR, 2019

Practical and Consistent Estimation of f-Divergences.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When can unlabeled data improve the learning rate?
Proceedings of the Conference on Learning Theory, 2019

The Optimal Approximation Factor in Density Estimation.
Proceedings of the Conference on Learning Theory, 2019

2018
Gradient Descent Quantizes ReLU Network Features.
CoRR, 2018

Assessing Generative Models via Precision and Recall.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Are GANs Created Equal? A Large-Scale Study.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Wasserstein Auto-Encoders.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Better Text Understanding Through Image-To-Text Transfer.
CoRR, 2017

Critical Hyper-Parameters: No Random, No Cry.
CoRR, 2017

Toward Optimal Run Racing: Application to Deep Learning Calibration.
CoRR, 2017

AdaGAN: Boosting Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Approximation and Convergence Properties of Generative Adversarial Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2010
L'apprentissage statistique à grande échelle.
Monde des Util. Anal. Données, 2010

2009
Prototype Classification: Insights from Machine Learning.
Neural Comput., 2009

2007
Guest editorial: Learning theory.
Mach. Learn., 2007

Statistical properties of kernel principal component analysis.
Mach. Learn., 2007

Combining PAC-Bayesian and Generic Chaining Bounds.
J. Mach. Learn. Res., 2007

The Tradeoffs of Large Scale Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Learning using Large Datasets.
Proceedings of the Mining Massive Data Sets for Security, 2007

2006
Cascade Evaluation of Clustering Algorithms.
Proceedings of the Machine Learning: ECML 2006, 2006

2005
Kernel Methods for Measuring Independence.
J. Mach. Learn. Res., 2005

Maximal margin classification for metric spaces.
J. Comput. Syst. Sci., 2005

Evaluating Predictive Uncertainty Challenge.
Proceedings of the Machine Learning Challenges, 2005

Joint Kernel Maps.
Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

SSC: statistical subspace clustering.
Proceedings of the Extraction et gestion des connaissances (EGC'2005), 2005

Measuring Statistical Dependence with Hilbert-Schmidt Norms.
Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

Kernel Constrained Covariance for Dependence Measurement.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

Hilbertian Metrics and Positive Definite Kernels on Probability Measures.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
Kernel methods and their potential use in signal processing.
IEEE Signal Process. Mag., 2004

A Compression Approach to Support Vector Model Selection.
J. Mach. Learn. Res., 2004

Distance-Based Classification with Lipschitz Functions.
J. Mach. Learn. Res., 2004

Limits of Spectral Clustering.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Hilbertian Metrics on Probability Measures and Their Application in SVM?s.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

On the Convergence of Spectral Clustering on Random Samples: The Normalized Case.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003
Feature selection and transduction for prediction of molecular bioactivity for drug design.
Bioinform., 2003

Ranking on Data Manifolds.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Learning with Local and Global Consistency.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Measure Based Regularization.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

PAC-Bayesian Generic Chaining.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Kernel methods and their applications to signal processing.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

Maximal Margin Classification for Metric Spaces.
Proceedings of the Computational Learning Theory and Kernel Machines, 2003

Introduction to Statistical Learning Theory.
Proceedings of the Advanced Lectures on Machine Learning, 2003

Concentration Inequalities.
Proceedings of the Advanced Lectures on Machine Learning, 2003

2002
Choosing Multiple Parameters for Support Vector Machines.
Mach. Learn., 2002

Tracking a Small Set of Experts by Mixing Past Posteriors.
J. Mach. Learn. Res., 2002

Stability and Generalization.
J. Mach. Learn. Res., 2002

On the Complexity of Learning the Kernel Matrix.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Some Local Measures of Complexity of Convex Hulls and Generalization Bounds.
Proceedings of the Computational Learning Theory, 2002

Localized Rademacher Complexities.
Proceedings of the Computational Learning Theory, 2002

2000
Algorithmic Stability and Generalization Performance.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999
Spatial Learning and Localization in Rodents: A Computational Model of the Hippocampus and its Implications for Mobile Robots.
Adapt. Behav., 1999


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