Detecting Myocardial Infarction Using Medial Surfaces - LV Statistical Modelling Challenge: Myocardial Infarction. - Pierre Ablin; Kaleem Siddiqi (STACOM@MICCAI, 2015)

Accelerating Likelihood Optimization for ICA on Real Signals. - Pierre Ablin; Jean-François Cardoso; Alexandre Gramfort (LVA/ICA, 2018)

Faster ICA Under Orthogonal Constraint. - Pierre Ablin; Jean-François Cardoso; Alexandre Gramfort (ICASSP, 2018)

EM algorithms for ICA. - Pierre Ablin; Alexandre Gramfort; Jean-François Cardoso; Francis R. Bach (CoRR, 2018)

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge. - 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; et al. (IEEE J. Biomed. Health Informatics, 2018)

Faster Independent Component Analysis by Preconditioning With Hessian Approximations. - Pierre Ablin; Jean-François Cardoso; Alexandre Gramfort (IEEE Trans. Signal Process., 2018)

Stochastic algorithms with descent guarantees for ICA. - Pierre Ablin; Alexandre Gramfort; Jean-François Cardoso; Francis R. Bach (AISTATS, 2019)

Beyond Pham's algorithm for joint diagonalization. - Pierre Ablin; Jean-François Cardoso; Alexandre Gramfort (ESANN, 2019)

A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning. - Pierre Ablin; Dylan Fagot; Herwig Wendt; Alexandre Gramfort; Cédric Févotte (ICASSP, 2019)

Learning step sizes for unfolded sparse coding. - Pierre Ablin; Thomas Moreau 001; Mathurin Massias; Alexandre Gramfort (NeurIPS, 2019)

Manifold-regression to predict from MEG/EEG brain signals without source modeling. - David Sabbagh; Pierre Ablin; Gaël Varoquaux; Alexandre Gramfort; Denis A. Engemann (NeurIPS, 2019)

Super-efficiency of automatic differentiation for functions defined as a minimum. - Pierre Ablin; Gabriel Peyré; Thomas Moreau 001 (ICML, 2020)

Modeling Shared responses in Neuroimaging Studies through MultiView ICA. - Hugo Richard; Luigi Gresele; Aapo Hyvärinen; Bertrand Thirion; Alexandre Gramfort; Pierre Ablin (NeurIPS, 2020)

Deep orthogonal linear networks are shallow. - Pierre Ablin (CoRR, 2020)

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. - David Sabbagh; Pierre Ablin; Gaël Varoquaux; Alexandre Gramfort; Denis A. Engemann (NeuroImage, 2020)

Kernel Stein Discrepancy Descent. - Anna Korba; Pierre-Cyril Aubin-Frankowski; Szymon Majewski; Pierre Ablin (ICML, 2021)

Momentum Residual Neural Networks. - Michael E. Sander; Pierre Ablin; Mathieu Blondel; Gabriel Peyré (ICML, 2021)

Shared Independent Component Analysis for Multi-Subject Neuroimaging. - Hugo Richard; Pierre Ablin; Bertrand Thirion; Alexandre Gramfort; Aapo Hyvärinen (NeurIPS, 2021)

Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. - Hugo Richard; Pierre Ablin; Aapo Hyvärinen; Alexandre Gramfort; Bertrand Thirion (CoRR, 2021)

mvlearn: Multiview Machine Learning in Python. - 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 (J. Mach. Learn. Res., 2021)

Fast and accurate optimization on the orthogonal manifold without retraction. - Pierre Ablin; Gabriel Peyré (AISTATS, 2022)

Sinkformers: Transformers with Doubly Stochastic Attention. - Michael E. Sander; Pierre Ablin; Mathieu Blondel; Gabriel Peyré (AISTATS, 2022)

Benchopt: Reproducible, efficient and collaborative optimization benchmarks. - Thomas Moreau 001; 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 002; et al. (NeurIPS, 2022)

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms. - Mathieu Dagréou; Pierre Ablin; Samuel Vaiter; Thomas Moreau 001 (NeurIPS, 2022)

Do Residual Neural Networks discretize Neural Ordinary Differential Equations? - Michael E. Sander; Pierre Ablin; Gabriel Peyré (NeurIPS, 2022)

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. - Marco Cuturi; Michal Klein; Pierre Ablin (ICML, 2023)

Multiview Independent Component Analysis with Delays. - Ambroise Heurtebise; Pierre Ablin; Alexandre Gramfort (MLSP, 2023)

How to Scale Your EMA. - Dan Busbridge; Jason Ramapuram; Pierre Ablin; Tatiana Likhomanenko; Eeshan Gunesh Dhekane; Xavier Suau Cuadros; Russell Webb (NeurIPS, 2023)

A Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. - Mathieu Dagréou; Thomas Moreau 001; Samuel Vaiter; Pierre Ablin (CoRR, 2023)

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints. - Pierre Ablin; Simon Vary; Bin Gao 007; Pierre-Antoine Absil (CoRR, 2023)

Test like you Train in Implicit Deep Learning. - Zaccharie Ramzi; Pierre Ablin; Gabriel Peyré; Thomas Moreau 001 (CoRR, 2023)

Learning Costs for Structured Monge Displacements. - Michal Klein; Aram-Alexandre Pooladian; Pierre Ablin; Eugène Ndiaye; Jonathan Niles-Weed; Marco Cuturi (CoRR, 2023)

A Challenge in Reweighting Data with Bilevel Optimization. - Anastasia Ivanova; Pierre Ablin (CoRR, 2023)

Understanding the Regularity of Self-Attention with Optimal Transport. - Valérie Castin; Pierre Ablin; Gabriel Peyré (CoRR, 2023)

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization. - Mathieu Dagréou; Thomas Moreau 001; Samuel Vaiter; Pierre Ablin (AISTATS, 2024)

Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization. - Zhenzhang Ye; Gabriel Peyré; Daniel Cremers; Pierre Ablin (AISTATS, 2024)

How Smooth Is Attention? - Valérie Castin; Pierre Ablin; Gabriel Peyré (ICML, 2024)

Careful with that Scalpel: Improving Gradient Surgery with an EMA. - Yu-Guan Hsieh; James Thornton; Eugène Ndiaye; Michal Klein; Marco Cuturi; Pierre Ablin (ICML, 2024)

Optimization without Retraction on the Random Generalized Stiefel Manifold. - Simon Vary; Pierre Ablin; Bin Gao 007; Pierre-Antoine Absil (ICML, 2024)

Learning Elastic Costs to Shape Monge Displacements. - Michal Klein; Aram-Alexandre Pooladian; Pierre Ablin; Eugène Ndiaye; Jonathan Niles-Weed; Marco Cuturi (NeurIPS, 2024)

Specialized Language Models with Cheap Inference from Limited Domain Data. - David Grangier; Angelos Katharopoulos; Pierre Ablin; Awni Hannun (CoRR, 2024)

Dynamic Gradient Alignment for Online Data Mixing. - Simin Fan; David Grangier; Pierre Ablin (CoRR, 2024)

Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models. - Michael Kirchhof; James Thornton; Pierre Ablin; Louis Béthune; Eugène Ndiaye; Marco Cuturi (CoRR, 2024)

Adaptive Training Distributions with Scalable Online Bilevel Optimization. - David Grangier; Pierre Ablin; Awni Hannun (Trans. Mach. Learn. Res., 2024)

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling. - David Grangier; Simin Fan; Skyler Seto; Pierre Ablin (ICLR, 2025)

The AdEMAMix Optimizer: Better, Faster, Older. - Matteo Pagliardini; Pierre Ablin; David Grangier (ICLR, 2025)

Theory, Analysis, and Best Practices for Sigmoid Self-Attention. - Jason Ramapuram; Federico Danieli; Eeshan Gunesh Dhekane; Floris Weers; Dan Busbridge; Pierre Ablin; Tatiana Likhomanenko; Jagrit Digani; Zijin Gu; Amitis Shidani; Russell Webb (ICLR, 2025)

MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations. - Ambroise Heurtebise; Omar Chehab; Pierre Ablin; Alexandre Gramfort (CoRR, 2025)

A Unified Perspective on the Dynamics of Deep Transformers. - Valérie Castin; Pierre Ablin; José Antonio Carrillo 001; Gabriel Peyré (CoRR, 2025)

Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging. - Pierre Ablin; Angelos Katharopoulos; Skyler Seto; David Grangier (CoRR, 2025)

Scaling Laws for Forgetting during Finetuning with Pretraining Data Injection. - Louis Béthune; David Grangier; Dan Busbridge; Eleonora Gualdoni; Marco Cuturi; Pierre Ablin (CoRR, 2025)

Identifiable Multi-View Causal Discovery Without Non-Gaussianity. - Ambroise Heurtebise; Omar Chehab; Pierre Ablin; Alexandre Gramfort; Aapo Hyvärinen (CoRR, 2025)

The Geometries of Truth Are Orthogonal Across Tasks. - Waïss Azizian; Michael Kirchhof; Eugène Ndiaye; Louis Béthune; Michal Klein; Pierre Ablin; Marco Cuturi (CoRR, 2025)

Scaling Laws for Optimal Data Mixtures. - Mustafa Shukor; Louis Béthune; Dan Busbridge; David Grangier; Enrico Fini; Alaaeldin El-Nouby; Pierre Ablin (CoRR, 2025)