Fabian Pedregosa

According to our database, Fabian Pedregosa authored at least 57 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

When is Momentum Extragradient Optimal? A Polynomial-Based Analysis.

Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

Trans. Mach. Learn. Res., 2024

Stepping on the Edge: Curvature Aware Learning Rate Tuners.

Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa

CoRR, 2024

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.

Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon

CoRR, 2024

Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators.

Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan

CoRR, 2024

2023

Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis.

Courtney Paquette, Bart van Merriënboer, Elliot Paquette, Fabian Pedregosa

Found. Comput. Math., April, 2023

On the Interplay Between Stepsize Tuning and Progressive Sharpening.

Vincent Roulet, Atish Agarwala, Fabian Pedregosa

CoRR, 2023

Second-order regression models exhibit progressive sharpening to the edge of stability.

Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington

Proceedings of the International Conference on Machine Learning, 2023

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces.

Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Extragradient with Positive Momentum is Optimal for Games with Cross-Shaped Jacobian Spectrum.

Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

CoRR, 2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes.

Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa

CoRR, 2022

The Curse of Unrolling: Rate of Differentiating Through Optimization.

Damien Scieur, Gauthier Gidel, Quentin Bertrand, Fabian Pedregosa

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient and Modular Implicit Differentiation.

Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Implicit Bias in Overparameterized Bilevel Optimization.

Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse

Proceedings of the International Conference on Machine Learning, 2022

Only tails matter: Average-Case Universality and Robustness in the Convex Regime.

Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette

Proceedings of the International Conference on Machine Learning, 2022

GradMax: Growing Neural Networks using Gradient Information.

Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov

Proceedings of the Tenth International Conference on Learning Representations, 2022

Super-Acceleration with Cyclical Step-sizes.

Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Bridging the Gap Between Adversarial Robustness and Optimization Bias.

Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2021

Boosting Variational Inference With Locally Adaptive Step-Sizes.

Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Average-case Acceleration for Bilinear Games and Normal Matrices.

Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

Proceedings of the 9th International Conference on Learning Representations, 2021

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality.

Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette

Proceedings of the Conference on Learning Theory, 2021

2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.

Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa

CoRR, 2020

The Geometry of Sign Gradient Descent.

Lukas Balles, Fabian Pedregosa, Nicolas Le Roux

CoRR, 2020

Average-case Acceleration Through Spectral Density Estimation.

Fabian Pedregosa, Damien Scieur

CoRR, 2020

Universal Asymptotic Optimality of Polyak Momentum.

Damien Scieur, Fabian Pedregosa

Proceedings of the 37th International Conference on Machine Learning, 2020

Acceleration through spectral density estimation.

Fabian Pedregosa, Damien Scieur

Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.

Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa

Proceedings of the 37th International Conference on Machine Learning, 2020

On the interplay between noise and curvature and its effect on optimization and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Linearly Convergent Frank-Wolfe without Line-Search.

Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

A Test for Shared Patterns in Cross-modal Brain Activation Analysis.

Elena Kalinina, Fabian Pedregosa, Vittorio Iacovella, Emanuele Olivetti, Paolo Avesani

CoRR, 2019

SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python.

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, SciPy 1. 0 Contributors

CoRR, 2019

The Difficulty of Training Sparse Neural Networks.

Utku Evci, Fabian Pedregosa, Aidan N. Gomez, Erich Elsen

CoRR, 2019

Information matrices and generalization.

Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

CoRR, 2019

Proximal Splitting Meets Variance Reduction.

Fabian Pedregosa, Kilian Fatras, Mattia Casotto

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods.

Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

J. Mach. Learn. Res., 2018

Adaptive Three Operator Splitting.

Fabian Pedregosa, Gauthier Gidel

Proceedings of the 35th International Conference on Machine Learning, 2018

Frank-Wolfe with Subsampling Oracle.

Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont

Proceedings of the 35th International Conference on Machine Learning, 2018

Frank-Wolfe Splitting via Augmented Lagrangian Method.

Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

SymPy: symbolic computing in Python.

Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondrej Certík, Sergey B. Kirpichev, Matthew Rocklin, Amit Kumar, Sergiu Ivanov, Jason Keith Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E. Granger, Richard P. Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J. Curry, Andy R. Terrel, Stepán Roucka, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, Anthony M. Scopatz

PeerJ Comput. Sci., 2017

On the Consistency of Ordinal Regression Methods.

Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort

J. Mach. Learn. Res., 2017

Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization.

Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ASAGA: Asynchronous Parallel SAGA.

Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

SymPy: Symbolic computing in Python.

Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondrej Certík, Matthew Rocklin, Amit Kumar, Sergiu Ivanov, Jason Keith Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E. Granger, Richard P. Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J. Curry, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, Anthony M. Scopatz

PeerJ Prepr., 2016

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding.

Valentina Borghesani, Fabian Pedregosa, Marco Buiatti, Alexis Amadon, Evelyn Eger, Manuela Piazza

NeuroImage, 2016

Hyperparameter optimization with approximate gradient.

Fabian Pedregosa

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Scikit-learn: Machine Learning Without Learning the Machinery.

Gaël Varoquaux, Lars Buitinck, Gilles Louppe, Olivier Grisel, Fabian Pedregosa, Andreas Mueller

GetMobile Mob. Comput. Commun., 2015

Data-driven HRF estimation for encoding and decoding models.

Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, Alexandre Gramfort

NeuroImage, 2015

2014

Machine learning for neuroimaging with scikit-learn.

Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gaël Varoquaux

Frontiers Neuroinformatics, 2014

A perceptual-to-conceptual gradient of word coding along the ventral path.

Valentina Borghesani, Fabian Pedregosa, Evelyn Eger, Marco Buiatti, Manuela Piazza

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Decoding perceptual thresholds from MEG/EEG.

Yousra Bekhti, Nicolas Zilber, Fabian Pedregosa, Philippe Ciuciu, Virginie van Wassenhove, Alexandre Gramfort

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Automatic pathology classification using a single feature machine learning support - vector machines.

Fernando Yepes-Calderon, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, Natasha Leporé

Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, 2014

2013

API design for machine learning software: experiences from the scikit-learn project.

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, Gaël Varoquaux

CoRR, 2013

HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models.

Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures.

Michael Eickenberg, Mehdi Senoussi, Fabian Pedregosa, Alexandre Gramfort, Bertrand Thirion

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

2012

Improved Brain Pattern Recovery through Ranking Approaches.

Fabian Pedregosa, Elodie Cauvet, Gaël Varoquaux, Christophe Pallier, Bertrand Thirion, Alexandre Gramfort

Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Learning to Rank from Medical Imaging Data.

Fabian Pedregosa, Elodie Cauvet, Gaël Varoquaux, Christophe Pallier, Bertrand Thirion, Alexandre Gramfort

Proceedings of the Machine Learning in Medical Imaging - Third International Workshop, 2012

2011

Scikit-learn: Machine Learning in Python.

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay

J. Mach. Learn. Res., 2011

Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity.

Gaël Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, Bertrand Thirion

Proceedings of the Information Processing in Medical Imaging, 2011