Rodolphe Jenatton
27 papers · 2010–2024 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (60) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(11)
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Keyword Champion
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Keyword Pioneer
π
Triple Crown
π₯
Mega-Team Β· Squad
π
Renaissance Researcher
(5)
π₯
Unstoppable Β· Bronze
β
The Questioner
(2)
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Prolific Year Β· Productive
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Century Club Β· Bronze
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Trend Setter
ποΈ
Keyword Collector
(134)
Top co-authors
Research topics
Machine Learning
(25)
Optimization & Theory
(16)
Deep Learning
(11)
Artificial Intelligence
(10)
Core Methods
(10)
Learning Types Β· Machine Learning
(10)
Application Areas
(6)
Architectures
(5)
Core AI
(5)
Learning Types Β· Deep Learning
(5)
Mathematics & Optimization
(4)
Optimization
(4)
Bayesian & Probabilistic
(3)
Learning Paradigms
(3)
Models
(3)
Keywords
image classification
(5)
hyperparameter optimization
(4)
neural network
(4)
label noise
(4)
bayesian optimization
(3)
privileged information
(3)
structured sparsity
(3)
convex optimization
(3)
dictionary learning
(3)
mixture of expert
(3)
proximal operator
(2)
lipschitz constant
(2)
vision transformer
(2)
black-box optimization
(2)
gaussian process
(2)
sparsity-inducing norms
(2)
contrastive learning
(2)
uncertainty quantification
(2)
transfer learning
(2)
multimodal learning
(2)
Papers
Pi-DUAL: Using privileged information to distinguish clean from noisy labels
ICML 2024
Massively Scaling Heteroscedastic Classifiers
ICLR 2023
Scaling Vision Transformers to 22 Billion Parameters
ICML 2023
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
NeurIPS 2023
When does Privileged information Explain Away Label Noise?
ICML 2023
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
NeurIPS 2022
On Mixup Regularization
JMLR 2022
On the Adversarial Robustness of Mixture of Experts
NeurIPS 2022
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
AISTATS 2022
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
ICML 2022
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
CVPR 2021
Scaling Vision with Sparse Mixture of Experts
NeurIPS 2021
Training independent subnetworks for robust prediction
ICLR 2021
How Good is the Bayes Posterior in Deep Neural Networks Really?
ICML 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
NeurIPS 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
ICML 2020
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
NeurIPS 2019
Scalable Hyperparameter Transfer Learning
NeurIPS 2018
Bayesian Optimization with Tree-structured Dependencies
ICML 2017
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
ICML 2016
Convex Relaxations for Permutation Problems
NeurIPS 2013
A latent factor model for highly multi-relational data
NeurIPS 2012
Convex and Network Flow Optimization for Structured Sparsity
JMLR 2011
Proximal Methods for Hierarchical Sparse Coding
JMLR 2011
Structured Variable Selection with Sparsity-Inducing Norms
JMLR 2011
Network Flow Algorithms for Structured Sparsity
NeurIPS 2010
Structured Sparse Principal Component Analysis
AISTATS 2010