Pierre Ablin

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 58

Top Topics

Statistical Shape

Ordinary Differential Equations

Top Venues

Publications

Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. CoRR (2024)
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS (2024)
Simon Vary, Pierre Ablin, Bin Gao, Pierre-Antoine Absil
Optimization without Retraction on the Random Generalized Stiefel Manifold. CoRR (2024)
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. AISTATS (2024)
Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin
Careful with that Scalpel: Improving Gradient Surgery with an EMA. CoRR (2024)
David Grangier, Angelos Katharopoulos, Pierre Ablin, Awni Hannun
Specialized Language Models with Cheap Inference from Limited Domain Data. CoRR (2024)
David Grangier, Pierre Ablin, Awni Hannun
Adaptive Training Distributions with Scalable Online Bilevel Optimization. CoRR (2023)
Pierre Ablin, Simon Vary, Bin Gao, Pierre-Antoine Absil
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints. CoRR (2023)
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR (2023)
Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau, Russ Webb
How to Scale Your EMA. CoRR (2023)
Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau Cuadros, Russell Webb
How to Scale Your EMA. NeurIPS (2023)
Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort
Multiview Independent Component Analysis with Delays. MLSP (2023)
Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi
Learning Costs for Structured Monge Displacements. CoRR (2023)
Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort
MultiView Independent Component Analysis with Delays. CoRR (2023)
Anastasia Ivanova, Pierre Ablin
A Challenge in Reweighting Data with Bilevel Optimization. CoRR (2023)
Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré, Thomas Moreau
Test like you Train in Implicit Deep Learning. CoRR (2023)
Marco Cuturi, Michal Klein, Pierre Ablin
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. CoRR (2023)
Valérie Castin, Pierre Ablin, Gabriel Peyré
Understanding the Regularity of Self-Attention with Optimal Transport. CoRR (2023)
Marco Cuturi, Michal Klein, Pierre Ablin
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. ICML (2023)
Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. CoRR (2022)
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR (2022)
Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS (2022)
Pierre Ablin, Gabriel Peyré
Fast and accurate optimization on the orthogonal manifold without retraction. AISTATS (2022)
Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupré la Tour, Ghislain Durif, Cássio F. Dantas, Quentin Klopfenstein, Johan Larsson, En Lai, Tanguy Lefort, Benoît Malézieux, Badr Moufad, Binh T. Nguyen, Alain Rakotomamonjy, Zaccharie Ramzi, Joseph Salmon, Samuel Vaiter
Benchopt: Reproducible, efficient and collaborative optimization benchmarks. NeurIPS (2022)
Michael E. Sander, Pierre Ablin, Gabriel Peyré
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? CoRR (2022)
Michael E. Sander, Pierre Ablin, Gabriel Peyré
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? NeurIPS (2022)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS (2022)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Momentum Residual Neural Networks. CoRR (2021)
Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin
Kernel Stein Discrepancy Descent. CoRR (2021)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Momentum Residual Neural Networks. ICML (2021)
Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS (2021)
Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR (2021)
Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin
Kernel Stein Discrepancy Descent. ICML (2021)
Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR (2021)
Pierre Ablin, Gabriel Peyré
Fast and accurate optimization on the orthogonal manifold without retraction. CoRR (2021)
Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR (2021)
Ronan Perry, Gavin Mischler, Richard Guo, Theo Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein
mvlearn: Multiview Machine Learning in Python. J. Mach. Learn. Res. 22 (2021)
Pierre Ablin, Gabriel Peyré, Thomas Moreau
Super-efficiency of automatic differentiation for functions defined as a minimum. ICML (2020)
Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR (2020)
Pierre Ablin, Gabriel Peyré, Thomas Moreau
Super-efficiency of automatic differentiation for functions defined as a minimum. CoRR (2020)
Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS (2020)
David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage 222 (2020)
Pierre Ablin
Deep orthogonal linear networks are shallow. CoRR (2020)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Beyond Pham's algorithm for joint diagonalization. ESANN (2019)
David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann
Manifold-regression to predict from MEG/EEG brain signals without source modeling. NeurIPS (2019)
Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort
Learning step sizes for unfolded sparse coding. CoRR (2019)
Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach
Stochastic algorithms with descent guarantees for ICA. AISTATS (2019)
Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte
A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning. ICASSP (2019)
Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort
Learning step sizes for unfolded sparse coding. NeurIPS (2019)
David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann
Manifold-regression to predict from MEG/EEG brain signals without source modeling. CoRR (2019)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Beyond Pham's algorithm for joint diagonalization. CoRR (2018)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Accelerating likelihood optimization for ICA on real signals. CoRR (2018)
Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach
EM algorithms for ICA. CoRR (2018)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Accelerating Likelihood Optimization for ICA on Real Signals. LVA/ICA (2018)
Avan Suinesiaputra, Pierre Ablin, Xènia Albà, Martino Alessandrini, Jack Allen, Wenjia Bai, Serkan Çimen, Peter Claes, Brett R. Cowan, Jan D'hooge, Nicolas Duchateau, Jan Ehrhardt, Alejandro F. Frangi, Ali Gooya, Vicente Grau, Karim Lekadir, Allen Lu, Anirban Mukhopadhyay, Ilkay Öksüz, Nripesh Parajuli, Xavier Pennec, Marco Pereañez, Catarina Pinto, Paolo Piras, Marc-Michel Rohé, Daniel Rueckert, Dennis Säring, Maxime Sermesant, Kaleem Siddiqi, Mahdi Tabassian, Luciano Teresi, Sotirios A. Tsaftaris, Matthias Wilms, Alistair A. Young, Xingyu Zhang, Pau Medrano-Gracia
Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge. IEEE J. Biomed. Health Informatics 22 (2) (2018)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Faster ICA Under Orthogonal Constraint. ICASSP (2018)
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Faster Independent Component Analysis by Preconditioning With Hessian Approximations. IEEE Trans. Signal Process. 66 (15) (2018)
Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning. CoRR (2018)
Pierre Ablin, Kaleem Siddiqi
Detecting Myocardial Infarction Using Medial Surfaces - LV Statistical Modelling Challenge: Myocardial Infarction. STACOM@MICCAI (2015)