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2020 – today
- 2025
[i42]Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort:
MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations. CoRR abs/2501.07426 (2025)- [i41]Valérie Castin, Pierre Ablin, José Antonio Carrillo, Gabriel Peyré:
A Unified Perspective on the Dynamics of Deep Transformers. CoRR abs/2501.18322 (2025) - [i40]Pierre Ablin, Angelos Katharopoulos, Skyler Seto, David Grangier:
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging. CoRR abs/2502.01804 (2025) - [i39]Louis Béthune, David Grangier, Dan Busbridge, Eleonora Gualdoni, Marco Cuturi, Pierre Ablin:
Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection. CoRR abs/2502.06042 (2025) - [i38]Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyvärinen:
Identifiable Multi-View Causal Discovery Without Non-Gaussianity. CoRR abs/2502.20115 (2025) - 2024
- [c27]Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. AISTATS 2024: 82-90 - [c26]Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin:
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. AISTATS 2024: 955-963 - [c25]Valérie Castin, Pierre Ablin, Gabriel Peyré:
How Smooth Is Attention? ICML 2024 - [c24]Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin:
Careful with that Scalpel: Improving Gradient Surgery with an EMA. ICML 2024 - [c23]Simon Vary, Pierre Ablin, Bin Gao, Pierre-Antoine Absil:
Optimization without Retraction on the Random Generalized Stiefel Manifold. ICML 2024 - [c22]Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi:
Learning Elastic Costs to Shape Monge Displacements. NeurIPS 2024 - [i37]David Grangier, Angelos Katharopoulos, Pierre Ablin, Awni Hannun:
Specialized Language Models with Cheap Inference from Limited Domain Data. CoRR abs/2402.01093 (2024) - [i36]Yu-Guan Hsieh, James Thornton, Eugène Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin:
Careful with that Scalpel: Improving Gradient Surgery with an EMA. CoRR abs/2402.02998 (2024) - [i35]Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin:
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. CoRR abs/2402.16748 (2024) - [i34]Simon Vary, Pierre Ablin, Bin Gao, Pierre-Antoine Absil:
Optimization without Retraction on the Random Generalized Stiefel Manifold. CoRR abs/2405.01702 (2024) - [i33]Matteo Pagliardini, Pierre Ablin, David Grangier:
The AdEMAMix Optimizer: Better, Faster, Older. CoRR abs/2409.03137 (2024) - [i32]Jason Ramapuram, Federico Danieli, Eeshan Gunesh Dhekane, Floris Weers, Dan Busbridge, Pierre Ablin, Tatiana Likhomanenko, Jagrit Digani, Zijin Gu, Amitis Shidani, Russ Webb:
Theory, Analysis, and Best Practices for Sigmoid Self-Attention. CoRR abs/2409.04431 (2024) - [i31]Simin Fan, David Grangier, Pierre Ablin:
Dynamic Gradient Alignment for Online Data Mixing. CoRR abs/2410.02498 (2024) - [i30]David Grangier, Simin Fan, Skyler Seto, Pierre Ablin:
Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling. CoRR abs/2410.03735 (2024) - [i29]Michael Kirchhof, James Thornton, Pierre Ablin, Louis Béthune, Eugène Ndiaye, Marco Cuturi:
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models. CoRR abs/2410.06025 (2024) - 2023
- [c21]Marco Cuturi, Michal Klein, Pierre Ablin:
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. ICML 2023: 6671-6682 - [c20]Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort:
Multiview Independent Component Analysis with Delays. MLSP 2023: 1-6 - [c19]Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau Cuadros, Russell Webb:
How to Scale Your EMA. NeurIPS 2023 - [i28]Marco Cuturi, Michal Klein, Pierre Ablin:
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. CoRR abs/2302.04065 (2023) - [i27]Mathieu Dagréou
, Thomas Moreau, Samuel Vaiter, Pierre Ablin:
A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. CoRR abs/2302.08766 (2023) - [i26]Pierre Ablin, Simon Vary, Bin Gao, Pierre-Antoine Absil:
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints. CoRR abs/2303.16510 (2023) - [i25]Zaccharie Ramzi, Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Test like you Train in Implicit Deep Learning. CoRR abs/2305.15042 (2023) - [i24]Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi:
Learning Costs for Structured Monge Displacements. CoRR abs/2306.11895 (2023) - [i23]Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau, Russ Webb:
How to Scale Your EMA. CoRR abs/2307.13813 (2023) - [i22]Anastasia Ivanova, Pierre Ablin:
A Challenge in Reweighting Data with Bilevel Optimization. CoRR abs/2310.17386 (2023) - [i21]David Grangier, Pierre Ablin, Awni Hannun:
Adaptive Training Distributions with Scalable Online Bilevel Optimization. CoRR abs/2311.11973 (2023) - [i20]Ambroise Heurtebise, Pierre Ablin, Alexandre Gramfort:
MultiView Independent Component Analysis with Delays. CoRR abs/2312.00484 (2023) - [i19]Valérie Castin, Pierre Ablin, Gabriel Peyré:
Understanding the Regularity of Self-Attention with Optimal Transport. CoRR abs/2312.14820 (2023) - 2022
- [c18]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. AISTATS 2022: 3515-3530 - [c17]Pierre Ablin, Gabriel Peyré:
Fast and accurate optimization on the orthogonal manifold without retraction. AISTATS 2022: 5636-5657 - [c16]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 - [c15]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. NeurIPS 2022 - [c14]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? NeurIPS 2022 - [i18]Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau:
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. CoRR abs/2201.13409 (2022) - [i17]Michael E. Sander, Pierre Ablin, Gabriel Peyré:
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? CoRR abs/2205.14612 (2022) - [i16]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 abs/2206.13424 (2022) - 2021
- [j4]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: 109:1-109:7 (2021) - [c13]Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin:
Kernel Stein Discrepancy Descent. ICML 2021: 5719-5730 - [c12]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. ICML 2021: 9276-9287 - [c11]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS 2021: 29962-29971 - [i15]Pierre Ablin, Gabriel Peyré:
Fast and accurate optimization on the orthogonal manifold without retraction. CoRR abs/2102.07432 (2021) - [i14]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Momentum Residual Neural Networks. CoRR abs/2102.07870 (2021) - [i13]Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion:
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR abs/2102.10964 (2021) - [i12]Anna Korba, Pierre-Cyril Aubin-Frankowski, Szymon Majewski, Pierre Ablin:
Kernel Stein Discrepancy Descent. CoRR abs/2105.09994 (2021) - [i11]Michael E. Sander, Pierre Ablin, Mathieu Blondel, Gabriel Peyré:
Sinkformers: Transformers with Doubly Stochastic Attention. CoRR abs/2110.11773 (2021) - [i10]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR abs/2110.13502 (2021) - 2020
- [j3]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: 116893 (2020) - [c10]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. ICML 2020: 32-41 - [c9]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS 2020 - [i9]Pierre Ablin, Gabriel Peyré, Thomas Moreau:
Super-efficiency of automatic differentiation for functions defined as a minimum. CoRR abs/2002.03722 (2020) - [i8]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR abs/2006.06635 (2020) - [i7]Pierre Ablin:
Deep orthogonal linear networks are shallow. CoRR abs/2011.13831 (2020)
2010 – 2019
- 2019
- [c8]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
Stochastic algorithms with descent guarantees for ICA. AISTATS 2019: 1564-1573 - [c7]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Beyond Pham's algorithm for joint diagonalization. ESANN 2019 - [c6]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: 700-704 - [c5]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: 7321-7332 - [c4]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. NeurIPS 2019: 13100-13110 - [i6]Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort:
Learning step sizes for unfolded sparse coding. CoRR abs/1905.11071 (2019) - [i5]David Sabbagh, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, Denis A. Engemann:
Manifold-regression to predict from MEG/EEG brain signals without source modeling. CoRR abs/1906.02687 (2019) - 2018
- [j2]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): 503-515 (2018) - [j1]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Faster Independent Component Analysis by Preconditioning With Hessian Approximations. IEEE Trans. Signal Process. 66(15): 4040-4049 (2018) - [c3]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Accelerating Likelihood Optimization for ICA on Real Signals. LVA/ICA 2018: 151-160 - [c2]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Faster ICA Under Orthogonal Constraint. ICASSP 2018: 4464-4468 - [i4]Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis R. Bach:
EM algorithms for ICA. CoRR abs/1805.10054 (2018) - [i3]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Accelerating likelihood optimization for ICA on real signals. CoRR abs/1806.09390 (2018) - [i2]Pierre Ablin, Dylan Fagot, Herwig Wendt, Alexandre Gramfort, Cédric Févotte:
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning. CoRR abs/1811.02225 (2018) - [i1]Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort:
Beyond Pham's algorithm for joint diagonalization. CoRR abs/1811.11433 (2018) - 2015
- [c1]Pierre Ablin, Kaleem Siddiqi:
Detecting Myocardial Infarction Using Medial Surfaces - LV Statistical Modelling Challenge: Myocardial Infarction. STACOM@MICCAI 2015: 146-153
Coauthor Index
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