Rodolphe Jenatton
27 papers · 2010–2024 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (21) π Interdisciplinary Bridge π Renaissance Researcher (6) π Conference Polyglot (6)
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(8)
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Taxonomy Completionist
(21)
π₯
Mega-Team
(42)
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Triple Crown
π¬
Deep Specialist
(11)
π
Keyword Champion
π
Century Club
(27)
β‘
Prolific Year
(5)
π
Conference Pioneer
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Trend Setter
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The Questioner
(2)
π₯
Unstoppable
(9)
ποΈ
Keyword Collector
(120)
Conferences
NIPS (10)
ICML (8)
JMLR (4)
AISTATS (2)
ICLR (2)
CVPR (1)
Top co-authors
Keywords
hyperparameter optimization
(4)
image classification
(4)
neural network
(4)
mixture of expert
(3)
structured sparsity
(3)
bayesian optimization
(3)
dictionary learning
(3)
label noise
(3)
lipschitz constant
(2)
privileged information
(2)
proximal operator
(2)
vision transformer
(2)
sparsity-inducing norms
(2)
black-box optimization
(2)
network flow
(2)
convex optimization
(2)
contrastive learning
(2)
uncertainty quantification
(2)
transfer learning
(2)
gaussian process
(2)
Papers
Pi-DUAL: Using privileged information to distinguish clean from noisy labels
ICML 2024
Massively Scaling Heteroscedastic Classifiers
ICLR 2023
When does Privileged information Explain Away Label Noise?
ICML 2023
Scaling Vision Transformers to 22 Billion Parameters
ICML 2023
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
NIPS 2023
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
ICML 2022
On Mixup Regularization
JMLR 2022
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
AISTATS 2022
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
NIPS 2022
On the Adversarial Robustness of Mixture of Experts
NIPS 2022
Training independent subnetworks for robust prediction
ICLR 2021
Scaling Vision with Sparse Mixture of Experts
NIPS 2021
Correlated Input-Dependent Label Noise in Large-Scale Image Classification
CVPR 2021
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
ICML 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
NIPS 2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
ICML 2020
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
NIPS 2019
Scalable Hyperparameter Transfer Learning
NIPS 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
NIPS 2013
A latent factor model for highly multi-relational data
NIPS 2012
Proximal Methods for Hierarchical Sparse Coding
JMLR 2011
Convex and Network Flow Optimization for Structured Sparsity
JMLR 2011
Structured Variable Selection with Sparsity-Inducing Norms
JMLR 2011
Structured Sparse Principal Component Analysis
AISTATS 2010
Network Flow Algorithms for Structured Sparsity
NIPS 2010