Olivier Bousquet

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

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Achievements

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🗺️ Taxonomy Completionist (45) 🏃 Academic Marathon · Ultra 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird

🌱 Topic Pioneer 🌟 Keyword Trendsetter Combo (7) 🔬 Deep Specialist (12) 🏆 Keyword Champion 🧭 Keyword Pioneer 📈 Trend Setter ⚡ Prolific Year · Productive 🗃️ Keyword Collector (131) ❓ The Questioner (3) 💎 Century Club · Bronze 🚀 Conference Pioneer

Venues

NeurIPS (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)

Research topics

Machine Learning (23) Optimization & Theory (16) Core Methods (10) Deep Learning (8) Artificial Intelligence (6) Core AI (6) Learning Types · Machine Learning (5) Models (5) Architectures (3) Mathematics & Optimization (2) Natural Language Processing (2) Learning Types · Deep Learning (2) Mathematics (2) Resources & Methods (2) Reinforcement Learning (1)

Keywords

generative adversarial network (4) support vector machine (4) pac learning (3) generative model (3) generalization bound (3) compositional generalization (3) vc dimension (2) precision and recall (2) improper learning (2) statistical learning theory (2) learning theory (2) mutual information (2) least-to-most prompting (2) sample complexity (2) representation learning (2) rademacher complexity (2) gradient-based optimization (1) hyperparameter optimization (1) semi-supervised learning (1) policy gradient (1)

Papers

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