Rodolphe Jenatton

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 48

Top Venues

CoRR

ICML

NeurIPS

KDD

École normale supérieure de Cachan, France (PHD)

Education History

École normale supérieure de Cachan, France

PHD

Publications

Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou
Massively Scaling Heteroscedastic Classifiers. CoRR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. CoRR (2023)
Ke Wang, Guillermo Ortiz-Jiménez, Rodolphe Jenatton, Mark Collier, Efi Kokiopoulou, Pascal Frossard
Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels. CoRR (2023)
Jannik Kossen, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Three Towers: Flexible Contrastive Learning with Pretrained Image Models. NeurIPS (2023)
Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander D'Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
When does Privileged Information Explain Away Label Noise? CoRR (2023)
Jannik Kossen, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou
Three Towers: Flexible Contrastive Learning with Pretrained Image Models. CoRR (2023)
Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Effrosyni Kokiopoulou
Massively Scaling Heteroscedastic Classifiers. ICLR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. ICML (2023)
Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D'Amour, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou
When does Privileged information Explain Away Label Noise? ICML (2023)
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert
On Mixup Regularization. J. Mach. Learn. Res. 23 (2022)
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, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022)
Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou, Jesse Berent
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. CoRR (2022)
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli
On the Adversarial Robustness of Mixture of Experts. CoRR (2022)
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts. CoRR (2022)
Mark Collier, Rodolphe Jenatton, Effrosyni Kokiopoulou, Jesse Berent
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information. ICML (2022)
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi, Srinadh Bhojanapalli
On the Adversarial Robustness of Mixture of Experts. NeurIPS (2022)
Basil Mustafa, Carlos Riquelme, Joan Puigcerver, Rodolphe Jenatton, Neil Houlsby
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts. NeurIPS (2022)
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR (2022)
Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS (2022)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022)
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby
Scaling Vision with Sparse Mixture of Experts. NeurIPS (2021)
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent
Correlated Input-Dependent Label Noise in Large-Scale Image Classification. CoRR (2021)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD (2021)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR (2021)
Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR (2021)
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent
Correlated Input-Dependent Label Noise in Large-Scale Image Classification. CVPR (2021)
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
Training independent subnetworks for robust prediction. ICLR (2021)
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, Neil Houlsby
Scaling Vision with Sparse Mixture of Experts. CoRR (2021)
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR (2021)
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, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. CoRR (2021)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR (2020)
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS (2020)
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert
On Mixup Regularization. CoRR (2020)
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR (2020)
Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR (2020)
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran
Training independent subnetworks for robust prediction. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR (2020)
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML (2020)
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR (2020)
Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel
Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR (2020)
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR (2019)
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR (2019)
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
Scalable Hyperparameter Transfer Learning. NeurIPS (2018)
Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger
Bayesian Optimization with Tree-structured Dependencies. ICML (2017)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. ICML (2016)
Jim C. Huang, Rodolphe Jenatton, Cédric Archambeau
Online Dual Decomposition for Performance and Delivery-Based Distributed Ad Allocation. KDD (2016)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Online optimization and regret guarantees for non-additive long-term constraints. CoRR (2016)
Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach, Martin Kleinsteuber, Matthias Seibert
Sample Complexity of Dictionary Learning and Other Matrix Factorizations. IEEE Trans. Inf. Theory 61 (6) (2015)
Fajwel Fogel, Rodolphe Jenatton, Francis R. Bach, Alexandre d'Aspremont
Convex Relaxations for Permutation Problems. SIAM J. Matrix Anal. Appl. 36 (4) (2015)
Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach
Sparse and Spurious: Dictionary Learning With Noise and Outliers. IEEE Trans. Inf. Theory 61 (11) (2015)
Antonino Freno, Martin Saveski, Rodolphe Jenatton, Cédric Archambeau
One-Pass Ranking Models for Low-Latency Product Recommendations. KDD (2015)
Rodolphe Jenatton, Jim C. Huang, Cédric Archambeau
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints. CoRR (2015)
Matthias Seibert, Martin Kleinsteuber, Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach
On the sample complexity of sparse dictionary learning. SSP (2014)
Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach
Sparse and spurious: dictionary learning with noise and outliers. CoRR (2014)
Rémi Gribonval, Rodolphe Jenatton, Francis R. Bach, Martin Kleinsteuber, Matthias Seibert
Sample Complexity of Dictionary Learning and other Matrix Factorizations. CoRR (2013)
Fajwel Fogel, Rodolphe Jenatton, Francis R. Bach, Alexandre d'Aspremont
Convex Relaxations for Permutation Problems. NIPS (2013)
Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski
A latent factor model for highly multi-relational data. NIPS (2012)
Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Evelyn Eger, Francis R. Bach, Bertrand Thirion
Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity. SIAM J. Imaging Sci. 5 (3) (2012)
Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski
Optimization with Sparsity-Inducing Penalties. Found. Trends Mach. Learn. 4 (1) (2012)
Rodolphe Jenatton, Rémi Gribonval, Francis R. Bach
Local stability and robustness of sparse dictionary learning in the presence of noise CoRR (2012)
Rodolphe Jenatton, Jean-Yves Audibert, Francis R. Bach
Structured Variable Selection with Sparsity-Inducing Norms. J. Mach. Learn. Res. 12 (2011)
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Convex and Network Flow Optimization for Structured Sparsity CoRR (2011)
Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski
Structured sparsity through convex optimization CoRR (2011)
Rodolphe Jenatton, Alexandre Gramfort, Vincent Michel, Guillaume Obozinski, Francis R. Bach, Bertrand Thirion
Multi-scale Mining of fMRI Data with Hierarchical Structured Sparsity. PRNI (2011)
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Convex and Network Flow Optimization for Structured Sparsity. J. Mach. Learn. Res. 12 (2011)
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis R. Bach
Proximal Methods for Hierarchical Sparse Coding. J. Mach. Learn. Res. 12 (2011)
Francis R. Bach, Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski
Optimization with Sparsity-Inducing Penalties CoRR (2011)
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Learning Hierarchical and Topographic Dictionaries with Structured Sparsity CoRR (2011)
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis R. Bach
Proximal Methods for Sparse Hierarchical Dictionary Learning. ICML (2010)
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Network Flow Algorithms for Structured Sparsity CoRR (2010)
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Network Flow Algorithms for Structured Sparsity. NIPS (2010)
Rodolphe Jenatton, Guillaume Obozinski, Francis R. Bach
Structured Sparse Principal Component Analysis. AISTATS (2010)

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Publication Activity (10 Years)

Top Topics

Top Venues

Rodolphe Jenatton