Gael Varoquaux
29 papers · 2010–2025 · 10 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (10) π Renaissance Researcher (5) π Interdisciplinary Bridge π§ Keyword Pioneer π Academic Marathon (15)
π
Academic Marathon
(15)
π
Cross-Pollinator
(12)
πΊοΈ
Taxonomy Completionist
(49)
π
Grand Slam
π
Keyword Champion
π₯
Mega-Team
(22)
π₯
Unstoppable
(7)
β
The Questioner
(3)
π
Trend Setter
ποΈ
Keyword Collector
(118)
π
Century Club
(29)
π
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
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
Whatβs 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