Olivier Bousquet

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

Achievements

+13 more ↓

🏃 Academic Marathon (21) 🌍 Conference Polyglot (6) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird

🌈 Renaissance Researcher (5) 🗺️ Taxonomy Completionist (36) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🌱 Topic Pioneer 🏆 Keyword Champion 🔬 Deep Specialist (11) 🗃️ Keyword Collector (113) ⚡ Prolific Year (7) 🚀 Conference Pioneer 📈 Trend Setter 💎 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 Schärli (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