← Authors

Léon Bottou

34 papers · 2005–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (20) 🗺️ Taxonomy Completionist (19) 🌍 Conference Polyglot (9) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (6) 🏆 Grand Slam 🔬 Deep Specialist (10) 🏆 Keyword Champion 🔥 Unstoppable (8) 🚀 Conference Pioneer 🗃️ Keyword Collector (61) ❓ The Questioner 💎 Century Club (34) 📈 Trend Setter

Conferences

JMLR (9) ICML (7) NIPS (6) AISTATS (3) CVPR (3) ICLR (3) AAAI (1) EMNLP (1) ICCV (1)

Top co-authors

Jianyu Zhang (6) David Lopez-paz (5) Antoine Bordes (4) Yann LeCun (3) Jason Weston (3) Nicolas Usunier (2) Vivien Cabannes (2) Maxime Oquab (2) Agnieszka Słowik (2) Bernhard Schölkopf (2)

Keywords

stochastic gradient descent (6) online learning (3) transfer learning (3) support vector machine (3) quasi-newton method (2) convolutional neural network (2) bias mitigation (2) causal inference (2) distributionally robust optimization (2) large scale learning (2) active learning (2) convergence rate (2) feature learning (2) representation learning (2) nonconvex optimization (2) stochastic optimization (2) object classification (2) out-of-distribution generalization (2) domain generalization (2) adaptive gradient (2)

Papers

Memory Mosaics ICLR 2025
MagicPIG: LSH Sampling for Efficient LLM Generation ICLR 2025
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization ICML 2023
Birth of a Transformer: A Memory Viewpoint NIPS 2023
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need ICCV 2023
Learning useful representations for shifting tasks and distributions ICML 2023
On the Relation between Distributionally Robust Optimization and Data Curation (Student Abstract) AAAI 2022
The Effects of Regularization and Data Augmentation are Class Dependent NIPS 2022
Rich Feature Construction for the Optimization-Generalization Dilemma ICML 2022
On Distributionally Robust Optimization and Data Rebalancing AISTATS 2022
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes JMLR 2020
Symplectic Recurrent Neural Networks ICLR 2020
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension ICML 2019
Cold Case: The Lost MNIST Digits NIPS 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NIPS 2019
AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes ICML 2019
An efficient distributed learning algorithm based on effective local functional approximations JMLR 2018
SING: Symbol-to-Instrument Neural Generator NIPS 2018
Discovering Causal Signals in Images CVPR 2017
Wasserstein Generative Adversarial Networks ICML 2017
No Regret Bound for Extreme Bandits AISTATS 2016
A Lower Bound for the Optimization of Finite Sums ICML 2015
Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks CVPR 2015
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks CVPR 2014
Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics EMNLP 2014
Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising JMLR 2013
Natural Language Processing (Almost) from Scratch JMLR 2011
Erratum: SGDQN is Less Careful than Expected JMLR 2010
Guarantees for Approximate Incremental SVMs AISTATS 2010
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent JMLR 2009
The Tradeoffs of Large Scale Learning NIPS 2007
The Need for Open Source Software in Machine Learning JMLR 2007
Large Scale Transductive SVMs JMLR 2006
Fast Kernel Classifiers with Online and Active Learning JMLR 2005