← Authors

Olivier Bousquet

26 papers · 2002–2023 · 6 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (11) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (21) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (36) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🌱 Topic Pioneer πŸ† Keyword Champion πŸ”¬ Deep Specialist (11) πŸ—ƒοΈ Keyword Collector (113) ⚑ Prolific Year (7) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (26) ❓ The Questioner (3)

Conferences

NIPS (8) JMLR (7) COLT (5) ICLR (4) AAAI (1) AISTATS (1)

Top co-authors

Sylvain Gelly (8) Shay Moran (4) Ilya O Tolstikhin (3) Nathanael SchΓ€rli (3) Nathan Scales (3) Ilya Tolstikhin (3) Olivier Bachem (3) Mario Lucic (3) Karol Kurach (2) Nikita Zhivotovskiy (2)

Keywords

support vector machine (4) generative adversarial network (4) generalization bound (3) pac learning (2) vc dimension (2) precision and recall (2) generative model (2) learning theory (2) mutual information (2) rademacher complexity (2) representation learning (2) gradient-based optimization (1) model selection (1) large margin (1) hyperparameter optimization (1) policy gradient (1) semi-supervised learning (1) density estimation (1) computational complexity (1) statistical learning theory (1)

Papers

Fine-Grained Distribution-Dependent Learning Curves COLT 2023
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima JMLR 2023
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models ICLR 2023
Compositional Semantic Parsing with Large Language Models ICLR 2023
Sharper Bounds for Uniformly Stable Algorithms COLT 2020
Synthetic Data Generators -- Sequential and Private NIPS 2020
What Do Neural Networks Learn When Trained With Random Labels? NIPS 2020
Google Research Football: A Novel Reinforcement Learning Environment AAAI 2020
Precision-Recall Curves Using Information Divergence Frontiers AISTATS 2020
Proper Learning, Helly Number, and an Optimal SVM Bound COLT 2020
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data ICLR 2020
When can unlabeled data improve the learning rate? COLT 2019
Practical and Consistent Estimation of f-Divergences NIPS 2019
The Optimal Approximation Factor in Density Estimation COLT 2019
Assessing Generative Models via Precision and Recall NIPS 2018
Wasserstein Auto-Encoders ICLR 2018
Are GANs Created Equal? A Large-Scale Study NIPS 2018
Approximation and Convergence Properties of Generative Adversarial Learning NIPS 2017
AdaGAN: Boosting Generative Models NIPS 2017
The Tradeoffs of Large Scale Learning NIPS 2007
Combining PAC-Bayesian and Generic Chaining Bounds JMLR 2007
Kernel Methods for Measuring Independence JMLR 2005
A Compression Approach to Support Vector Model Selection JMLR 2004
Distance-Based Classification with Lipschitz Functions JMLR 2004
Tracking a Small Set of Experts by Mixing Past Posteriors JMLR 2002
Stability and Generalization JMLR 2002