← Authors

Gael Varoquaux

29 papers · 2010–2025 · 10 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (10) πŸƒ Academic Marathon (15) 🐝 Cross-Pollinator (12) πŸ—ΊοΈ Taxonomy Completionist (49) πŸ† Grand Slam πŸ† Keyword Champion πŸ‘₯ Mega-Team (22) πŸ”₯ Unstoppable (7) ❓ The Questioner (3) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (118) πŸ’Ž Century Club (29) πŸš€ Conference Pioneer

Conferences

NIPS (11) ICML (5) AISTATS (3) EACL (2) EMNLP (2) ICLR (2) AAAI (1) ACL (1) JMLR (1) MICCAI (1)

Top co-authors

Bertrand Thirion (8) Marine Le Morvan (7) Lihu Chen (6) Erwan Scornet (3) Julie Josse (3) Olivier Grisel (3) Alexandre Gramfort (3) Alexandre Perez-Lebel (3) Fabian Suchanek (3) Fabian M. Suchanek (3)

Keywords

brain imaging (4) language model (3) neural network (3) contrastive learning (2) representation learning (2) dictionary learning (2) cognitive neuroscience (2) graphical model (2) stochastic gradient descent (1) sparse coding (1) fmri analysis (1) logistic regression (1) sparse representation (1) attention mechanism (1) matrix factorization (1) confidence calibration (1) feature extraction (1) text representation (1) supervised learning (1) multi-task learning (1)

Papers

Decision from Suboptimal Classifiers: Excess Risk Pre- and Post-Calibration AISTATS 2025
TabICL: A Tabular Foundation Model for In-Context Learning on Large Data ICML 2025
Imputation for prediction: beware of diminishing returns. ICLR 2025
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks AISTATS 2025
Confidence intervals uncovered: Are we ready for real-world medical imaging AI? MICCAI 2024
Learning High-Quality and General-Purpose Phrase Representations EACL 2024
Reconfidencing LLMs from the Grouping Loss Perspective EMNLP 2024
CARTE: Pretraining and Transfer for Tabular Learning ICML 2024
GLADIS: A General and Large Acronym Disambiguation Benchmark EACL 2023
Beyond calibration: estimating the grouping loss of modern neural networks ICLR 2023
The Locality and Symmetry of Positional Encodings EMNLP 2023
Why do tree-based models still outperform deep learning on typical tabular data? NIPS 2022
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost ACL 2022
A Lightweight Neural Model for Biomedical Entity Linking AAAI 2021
What’s a good imputation to predict with missing values? NIPS 2021
NeuMiss networks: differentiable programming for supervised learning with missing values. NIPS 2020
Linear predictor on linearly-generated data with missing values: non consistency and solutions AISTATS 2020
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data ICML 2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling NIPS 2019
Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing NIPS 2019
Learning to Discover Sparse Graphical Models ICML 2017
Learning Neural Representations of Human Cognition across Many fMRI Studies NIPS 2017
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity NIPS 2016
Dictionary Learning for Massive Matrix Factorization ICML 2016
Learning brain regions via large-scale online structured sparse dictionary learning NIPS 2016
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data NIPS 2015
Mapping paradigm ontologies to and from the brain NIPS 2013
Scikit-learn: Machine Learning in Python JMLR 2011
Brain covariance selection: better individual functional connectivity models using population prior NIPS 2010