Ablin, Pierre

35 publications

ICML 2025 Scaling Laws for Forgetting During Finetuning with Pretraining Data Injection Louis Béthune, David Grangier, Dan Busbridge, Eleonora Gualdoni, Marco Cuturi, Pierre Ablin

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

ICML 2025 Shielded Diffusion: Generating Novel and Diverse Images Using Sparse Repellency Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye, Marco Cuturi

ICML 2025 Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging Pierre Ablin, Angelos Katharopoulos, Skyler Seto, David Grangier

ICLRW 2025 Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging Pierre Ablin, Angelos Katharopoulos, Skyler Seto, David Grangier

ICLR 2025 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

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

NeurIPSW 2024 AdEMAMix: Better and Faster Training with Older Gradients Matteo Pagliardini, Pierre Ablin, David Grangier

TMLR 2024 Adaptive Training Distributions with Scalable Online Bilevel Optimization David Grangier, Pierre Ablin, Awni Hannun

ICML 2024 Careful with That Scalpel: Improving Gradient Surgery with an EMA Yu-Guan Hsieh, James Thornton, Eugene Ndiaye, Michal Klein, Marco Cuturi, Pierre Ablin

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

ICML 2024 How Smooth Is Attention? Valérie Castin, Pierre Ablin, Gabriel Peyré

JMLR 2024 Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization Under Orthogonality Constraints Pierre Ablin, Simon Vary, Bin Gao, Pierre-Antoine Absil

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

ICML 2024 Optimization Without Retraction on the Random Generalized Stiefel Manifold Simon Vary, Pierre Ablin, Bin Gao, Pierre-Antoine Absil

ICMLW 2024 Projected Language Models: A Large Model Pre-Segmented into Smaller Ones David Grangier, Angelos Katharopoulos, Pierre Ablin, Awni Hannun

NeurIPSW 2023 Bilevel Optimization to Learn Training Distributions for Language Modeling Under Domain Shift David Grangier, Pierre Ablin, Awni Hannun

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

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

AISTATS 2022 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

NeurIPS 2022 A Framework for Bilevel Optimization That Enables Stochastic and Global Variance Reduction Algorithms Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau

NeurIPS 2022 Benchopt: Reproducible, Efficient and Collaborative Optimization Benchmarks Thomas Moreau, Mathurin Massias, Alexandre Gramfort, Pierre Ablin, Pierre-Antoine Bannier, Benjamin Charlier, Mathieu Dagréou, Tom Dupre la Tour, Ghislain Durif, Cassio 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

NeurIPS 2022 Do Residual Neural Networks Discretize Neural Ordinary Differential Equations? Michael Sander, Pierre Ablin, Gabriel Peyré

ICML 2021 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

MLOSS 2021 Mvlearn: Multiview Machine Learning in Python Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein

NeurIPS 2021 Shared Independent Component Analysis for Multi-Subject Neuroimaging Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvarinen

NeurIPS 2020 Modeling Shared Responses in Neuroimaging Studies Through MultiView ICA Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

ICML 2020 Super-Efficiency of Automatic Differentiation for Functions Defined as a Minimum Pierre Ablin, Gabriel Peyré, Thomas Moreau

NeurIPS 2019 Learning Step Sizes for Unfolded Sparse Coding Pierre Ablin, Thomas Moreau, Mathurin Massias, Alexandre Gramfort

NeurIPS 2019 Manifold-Regression to Predict from MEG/EEG Brain Signals Without Source Modeling David Sabbagh, Pierre Ablin, Gael Varoquaux, Alexandre Gramfort, Denis A. Engemann

AISTATS 2019 Stochastic Algorithms with Descent Guarantees for ICA Pierre Ablin, Alexandre Gramfort, Jean-François Cardoso, Francis Bach