Proximal Methods for Sparse Hierarchical Dictionary Learning. - Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski; Francis R. Bach (ICML, 2010)

Network Flow Algorithms for Structured Sparsity. - Julien Mairal; Rodolphe Jenatton; Guillaume Obozinski; Francis R. Bach (NIPS, 2010)

Structured Sparse Principal Component Analysis. - Rodolphe Jenatton; Guillaume Obozinski; Francis R. Bach (AISTATS, 2010)

Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity. - Rodolphe Jenatton; Alexandre Gramfort; Vincent Michel; Guillaume Obozinski; Francis R. Bach; Bertrand Thirion (PRNI, 2011)

Structured sparsity through convex optimization - Francis R. Bach; Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski (CoRR, 2011)

Learning Hierarchical and Topographic Dictionaries with Structured Sparsity - Julien Mairal; Rodolphe Jenatton; Guillaume Obozinski; Francis R. Bach (CoRR, 2011)

Structured Variable Selection with Sparsity-Inducing Norms. - Rodolphe Jenatton; Jean-Yves Audibert; Francis R. Bach (J. Mach. Learn. Res., 2011)

Proximal Methods for Hierarchical Sparse Coding. - Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski; Francis R. Bach (J. Mach. Learn. Res., 2011)

Convex and Network Flow Optimization for Structured Sparsity. - Julien Mairal; Rodolphe Jenatton; Guillaume Obozinski; Francis R. Bach (J. Mach. Learn. Res., 2011)

Structured Sparsity-Inducing Norms : Statistical and Algorithmic Properties with Applications to Neuroimaging. (Normes Parcimonieuses Structurées : Propriétés Statistiques et Algorithmiques avec Applications à l'Imagerie Cérébrale). - Rodolphe Jenatton (2011)

A latent factor model for highly multi-relational data. - Rodolphe Jenatton; Nicolas Le Roux; Antoine Bordes; Guillaume Obozinski (NIPS, 2012)

Local stability and robustness of sparse dictionary learning in the presence of noise - Rodolphe Jenatton; Rémi Gribonval; Francis R. Bach (CoRR, 2012)

Optimization with Sparsity-Inducing Penalties. - Francis R. Bach; Rodolphe Jenatton; Julien Mairal; Guillaume Obozinski (Found. Trends Mach. Learn., 2012)

Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity. - Rodolphe Jenatton; Alexandre Gramfort; Vincent Michel; Guillaume Obozinski; Evelyn Eger; Francis R. Bach; Bertrand Thirion (SIAM J. Imaging Sci., 2012)

On the sample complexity of sparse dictionary learning. - Matthias Seibert; Martin Kleinsteuber; Rémi Gribonval; Rodolphe Jenatton; Francis R. Bach (SSP, 2014)

One-Pass Ranking Models for Low-Latency Product Recommendations. - Antonino Freno; Martin Saveski; Rodolphe Jenatton; Cédric Archambeau (KDD, 2015)

Convex Relaxations for Permutation Problems. - Fajwel Fogel; Rodolphe Jenatton; Francis R. Bach; Alexandre d'Aspremont (SIAM J. Matrix Anal. Appl., 2015)

Sparse and Spurious: Dictionary Learning With Noise and Outliers. - Rémi Gribonval; Rodolphe Jenatton; Francis R. Bach (IEEE Trans. Inf. Theory, 2015)

Sample Complexity of Dictionary Learning and Other Matrix Factorizations. - Rémi Gribonval; Rodolphe Jenatton; Francis R. Bach; Martin Kleinsteuber; Matthias Seibert (IEEE Trans. Inf. Theory, 2015)

Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. - Rodolphe Jenatton; Jim C. Huang; Cédric Archambeau (ICML, 2016)

Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. - Jim C. Huang; Rodolphe Jenatton; Cédric Archambeau (KDD, 2016)

Online optimization and regret guarantees for non-additive long-term constraints. - Rodolphe Jenatton; Jim C. Huang; Cédric Archambeau (CoRR, 2016)

Bayesian Optimization with Tree-structured Dependencies. - Rodolphe Jenatton; Cédric Archambeau; Javier González 002; Matthias W. Seeger (ICML, 2017)

Scalable Hyperparameter Transfer Learning. - Valerio Perrone; Rodolphe Jenatton; Matthias W. Seeger; Cédric Archambeau (NeurIPS, 2018)

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. - Valerio Perrone; Huibin Shen; Matthias W. Seeger; Cédric Archambeau; Rodolphe Jenatton (CoRR, 2019)

Constrained Bayesian Optimization with Max-Value Entropy Search. - Valerio Perrone; Iaroslav Shcherbatyi; Rodolphe Jenatton; Cédric Archambeau; Matthias W. Seeger (CoRR, 2019)

The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. - Jakub Swiatkowski; Kevin Roth; Bastiaan S. Veeling; Linh Tran; Joshua V. Dillon; Jasper Snoek; Stephan Mandt; Tim Salimans; Rodolphe Jenatton; Sebastian Nowozin (ICML, 2020)

How Good is the Bayes Posterior in Deep Neural Networks Really? - Florian Wenzel; Kevin Roth; Bastiaan S. Veeling; Jakub Swiatkowski; Linh Tran; Stephan Mandt; Jasper Snoek; Tim Salimans; Rodolphe Jenatton; Sebastian Nowozin (ICML, 2020)

Hyperparameter Ensembles for Robustness and Uncertainty Quantification. - Florian Wenzel; Jasper Snoek; Dustin Tran; Rodolphe Jenatton (NeurIPS, 2020)

Hydra: Preserving Ensemble Diversity for Model Distillation. - Linh Tran; Bastiaan S. Veeling; Kevin Roth; Jakub Swiatkowski; Joshua V. Dillon; Jasper Snoek; Stephan Mandt; Tim Salimans; Sebastian Nowozin; Rodolphe Jenatton (CoRR, 2020)

Amazon SageMaker Autopilot: a white box AutoML solution at scale. - Piali Das; Valerio Perrone; Nikita Ivkin; Tanya Bansal; Zohar S. Karnin; Huibin Shen; Iaroslav Shcherbatyi; Yotam Elor; Wilton Wu; Aida Zolic; Thibaut Liénart; Alex Tang; et al. (CoRR, 2020)

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (CoRR, 2020)

Correlated Input-Dependent Label Noise in Large-Scale Image Classification. - Mark Collier; Basil Mustafa; Efi Kokiopoulou; Rodolphe Jenatton; Jesse Berent (CVPR, 2021)

Training independent subnetworks for robust prediction. - Marton Havasi; Rodolphe Jenatton; Stanislav Fort; Jeremiah Zhe Liu; Jasper Snoek; Balaji Lakshminarayanan; Andrew Mingbo Dai; Dustin Tran (ICLR, 2021)

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (KDD, 2021)

Scaling Vision with Sparse Mixture of Experts. - Carlos Riquelme; Joan Puigcerver; Basil Mustafa; Maxim Neumann; Rodolphe Jenatton; André Susano Pinto; Daniel Keysers; Neil Houlsby (NeurIPS, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. - Setareh Ariafar; Justin Gilmer; Zachary Nado; Jasper Snoek; Rodolphe Jenatton; George E. Dahl (AISTATS, 2022)

Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. - Mark Collier; Rodolphe Jenatton; Effrosyni Kokiopoulou; Jesse Berent (ICML, 2022)

Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts. - Basil Mustafa; Carlos Riquelme; Joan Puigcerver; Rodolphe Jenatton; Neil Houlsby (NeurIPS, 2022)

On the Adversarial Robustness of Mixture of Experts. - Joan Puigcerver; Rodolphe Jenatton; Carlos Riquelme; Pranjal Awasthi; Srinadh Bhojanapalli (NeurIPS, 2022)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

On Mixup Regularization. - Luigi Carratino; Moustapha Cissé; Rodolphe Jenatton; Jean-Philippe Vert (J. Mach. Learn. Res., 2022)

Sparse MoEs meet Efficient Ensembles. - James Urquhart Allingham; Florian Wenzel; Zelda E. Mariet; Basil Mustafa; Joan Puigcerver; Neil Houlsby; Ghassen Jerfel; Vincent Fortuin; Balaji Lakshminarayanan; Jasper Snoek; Dustin Tran; Carlos Riquelme Ruiz; et al. (Trans. Mach. Learn. Res., 2022)

Deep Classifiers with Label Noise Modeling and Distance Awareness. - Vincent Fortuin; Mark Collier; Florian Wenzel; James Urquhart Allingham; Jeremiah Zhe Liu; Dustin Tran; Balaji Lakshminarayanan; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (Trans. Mach. Learn. Res., 2022)

Massively Scaling Heteroscedastic Classifiers. - Mark Collier; Rodolphe Jenatton; Basil Mustafa; Neil Houlsby; Jesse Berent; Effrosyni Kokiopoulou (ICLR, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

When does Privileged information Explain Away Label Noise? - Guillermo Ortiz-Jiménez; Mark Collier; Anant Nawalgaria; Alexander Nicholas D'Amour; Jesse Berent; Rodolphe Jenatton; Efi Kokiopoulou (ICML, 2023)

Three Towers: Flexible Contrastive Learning with Pretrained Image Models. - Jannik Kossen; Mark Collier; Basil Mustafa; Xiao Wang 038; Xiaohua Zhai; Lucas Beyer; Andreas Steiner 001; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (NeurIPS, 2023)

Pi-DUAL: Using privileged information to distinguish clean from noisy labels. - Ke Wang; Guillermo Ortiz-Jiménez; Rodolphe Jenatton; Mark Collier; Efi Kokiopoulou; Pascal Frossard (ICML, 2024)