World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
27329
World Ranking
3940
National Ranking
1871

Overview

Olivier Bousquet is affiliated with Google (United States) and has contributed extensively to research intersecting computer science and environmental science. Their work spans multiple fields including artificial intelligence, atmospheric science, and global and planetary change. The primary areas of study encompass tropical and extratropical cyclones research, ocean waves and remote sensing, machine learning and algorithms, as well as marine conservation topics such as turtle biology.

The scientist has published in a variety of academic venues, with a notable number of papers appearing in arXiv (Cornell University). Other frequent publication venues include Atmosphere, Frontiers in Marine Science, Acta Neurochirurgica, and Endangered Species Research.

The main fields of study highlighted in their body of work are:

  • Computer Science
  • Environmental Science
  • Earth and Planetary Sciences

Subfields where their research is focused include:

  • Artificial Intelligence
  • Atmospheric Science
  • Global and Planetary Change
  • Oceanography
  • Nature and Landscape Conservation

Specific research topics addressed throughout their publications cover:

  • Tropical and Extratropical Cyclones Research
  • Ocean Waves and Remote Sensing
  • Machine Learning and Algorithms
  • Turtle Biology and Conservation
  • Climate variability and models
  • Ionosphere and magnetosphere dynamics
  • Machine Learning and Data Classification

Olivier Bousquet often collaborates with a recurrent group of coauthors, including Soline Bielli, Stéphane Ciccione, Shay Moran, Édouard Lees, and Julien Cattiaux.

Selected recent papers include:

  • Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, 2022, arXiv (Cornell University)
  • Google Research Football: A Novel Reinforcement Learning Environment, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Compositional Semantic Parsing with Large Language Models, 2022, arXiv (Cornell University)
  • Predicting Neural Network Accuracy from Weights, 2020, arXiv (Cornell University)
  • Projected Changes in the Southern Indian Ocean Cyclone Activity Assessed from High-Resolution Experiments and CMIP5 Models, 2020, Journal of Climate

Best Publications

  • Learning with Local and Global Consistency

    Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston

  • Choosing Multiple Parameters for Support Vector Machines

    Olivier Chapelle;Vladimir Vapnik;Olivier Bousquet;Sayan Mukherjee

  • Measuring statistical dependence with hilbert-schmidt norms

    Arthur Gretton;Olivier Bousquet;Alex Smola;Bernhard Schölkopf

  • The Tradeoffs of Large Scale Learning

    Olivier Bousquet;Léon Bottou

  • Stability and generalization

    Olivier Bousquet;André Elisseeff

  • Ranking on Data Manifolds

    Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet

  • Local Rademacher complexities

    Peter L. Bartlett;Olivier Bousquet;Shahar Mendelson

  • Are GANs Created Equal? A Large-Scale Study

    Mario Lucic;Karol Kurach;Marcin Michalski;Sylvain Gelly

  • Theory of classification : a survey of some recent advances

    Stéphane Boucheron;Olivier Bousquet;Gábor Lugosi

  • Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

    Unknown

  • Introduction to Statistical Learning Theory

    Olivier Bousquet;Stéphane Boucheron;Gábor Lugosi

  • Consistency of spectral clustering

    U von Luxburg;M Belkin;O Bousquet

  • Wasserstein Auto-Encoders

    Ilya O. Tolstikhin;Olivier Bousquet;Sylvain Gelly;Bernhard Schölkopf

  • Kernel Methods for Measuring Independence

    Arthur Gretton;Ralf Herbrich;Alexander Smola;Olivier Bousquet

  • A Bennett concentration inequality and its application to suprema of empirical processes

    Olivier Bousquet

  • Assessing Generative Models via Precision and Recall

    Mehdi S. M. Sajjadi;Olivier Bachem;Mario Lucic;Olivier Bousquet

  • Advanced Lectures on Machine Learning

    O Bousquet;U von Luxburg;G Rätsch

  • Wasserstein Auto-Encoders

    Ilya Tolstikhin;Olivier Bousquet;Sylvain Gelly;Bernhard Schoelkopf

  • Consistency of spectral clustering

    Ulrike von Luxburg;Mikhail Belkin;Olivier Bousquet

  • Google Research Football: A Novel Reinforcement Learning Environment

    Karol Kurach;Anton Raichuk;Piotr Michal Stanczyk;Michał Zając

  • Distance--Based Classification with Lipschitz Functions

    Ulrike von Luxburg;Olivier Bousquet

  • A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

    Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen

  • Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

    Daniel Keysers;Nathanael Schärli;Nathan Scales;Hylke Buisman

Frequent Co-Authors

Sylvain Gelly
Sylvain Gelly Google (United States)
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Mario Lucic
Mario Lucic Google (United States)
Ulrike von Luxburg
Ulrike von Luxburg University of Tübingen
Matthias Hein
Matthias Hein University of Tübingen
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Jason Weston
Jason Weston Facebook (United States)
Daniel Keysers
Daniel Keysers Google (United States)
Arthur Gretton
Arthur Gretton University College London
Gábor Lugosi
Gábor Lugosi Pompeu Fabra University

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