Publications (49)
Universal Average-Case Optimality of Polyak Momentum
Damien Scieur, Fabian Pedregosa
SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant +32
Proximal Splitting Meets Variance Reduction
Fabian Pedregosa, Kilian Fatras, Mattia Casotto
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
Sanjeev Raja, Ishan Amin, Fabian Pedregosa +1
On Implicit Bias in Overparameterized Bilevel Optimization
Paul Vicol, Jonathan Lorraine, Fabian Pedregosa +2
Linearly Convergent Frank-Wolfe with Backtracking Line-Search
Fabian Pedregosa, Geoffrey Negiar, Armin Askari +1
Machine Learning for Neuroimaging with Scikit-Learn
Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg +6
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa +12
Efficient and Modular Implicit Differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi +5
Super-Acceleration with Cyclical Step-sizes
Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut +2
HRF estimation improves sensitivity of fMRI encoding and decoding models
Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion +1
Frank-Wolfe with Subsampling Oracle
Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont
Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis
Courtney Paquette, Bart van Merriënboer, Elliot Paquette +1
Average-case Acceleration Through Spectral Density Estimation
Fabian Pedregosa, Damien Scieur
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Charline Le Lan, Joshua Greaves, Jesse Farebrother +4
Improved asynchronous parallel optimization analysis for stochastic incremental methods
Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien
The Curse of Unrolling: Rate of Differentiating Through Optimization
Damien Scieur, Quentin Bertrand, Gauthier Gidel +1
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method
Teodora Baluta, Pascal Lamblin, Daniel Tarlow +2
GradMax: Growing Neural Networks using Gradient Information
Utku Evci, Bart van Merriënboer, Thomas Unterthiner +2
Learning to rank from medical imaging data
Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux +3
Improved brain pattern recovery through ranking approaches
Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux +3
ASAGA: Asynchronous Parallel SAGA
Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien
Average-case Acceleration for Bilinear Games and Normal Matrices
Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur
Adaptive Three Operator Splitting
Fabian Pedregosa, Gauthier Gidel
Data-driven HRF estimation for encoding and decoding models
Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu +2
Stepping on the Edge: Curvature Aware Learning Rate Tuners
Vincent Roulet, Atish Agarwala, Jean-Bastien Grill +3
Hyperparameter optimization with approximate gradient
Fabian Pedregosa
The Geometry of Sign Gradient Descent
Lukas Balles, Fabian Pedregosa, Nicolas Le Roux
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime
Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa +2
Frank-Wolfe Splitting via Augmented Lagrangian Method
Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality
Courtney Paquette, Kiwon Lee, Fabian Pedregosa +1
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
Second-order regression models exhibit progressive sharpening to the edge of stability
Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington
Boosting Variational Inference With Locally Adaptive Step-Sizes
Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa +2
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes
Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou +1
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis
Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis +1
The Difficulty of Training Sparse Neural Networks
Utku Evci, Fabian Pedregosa, Aidan Gomez +1
On the Consistency of Ordinal Regression Methods
Fabian Pedregosa, Francis Bach, Alexandre Gramfort
On the Interplay Between Stepsize Tuning and Progressive Sharpening
Vincent Roulet, Atish Agarwala, Fabian Pedregosa
On the interplay between noise and curvature and its effect on optimization and generalization
Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer +3
How far away are truly hyperparameter-free learning algorithms?
Priya Kasimbeg, Vincent Roulet, Naman Agarwal +4
On the convergence rate of the three operator splitting scheme
Fabian Pedregosa
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
Geoffrey Négiar, Gideon Dresdner, Alicia Tsai +4
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien
A Test for Shared Patterns in Cross-modal Brain Activation Analysis
Elena Kalinina, Fabian Pedregosa, Vittorio Iacovella +2
Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort +16
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel +12
Bridging the Gap Between Adversarial Robustness and Optimization Bias
Fartash Faghri, Sven Gowal, Cristina Vasconcelos +3
Second order scattering descriptors predict fMRI activity due to visual textures
Michael Eickenberg, Fabian Pedregosa, Senoussi Mehdi +2