Fabian Pedregosa

Orcid: 0000-0003-4025-3953

According to our database1, Fabian Pedregosa authored at least 55 papers between 2011 and 2024.

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Bibliography

2024
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.
CoRR, 2024

Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators.
CoRR, 2024

2023
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis.
Found. Comput. Math., April, 2023

On the Interplay Between Stepsize Tuning and Progressive Sharpening.
CoRR, 2023

Second-order regression models exhibit progressive sharpening to the edge of stability.
Proceedings of the International Conference on Machine Learning, 2023

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces.
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.
CoRR, 2022

Cutting Some Slack for SGD with Adaptive Polyak Stepsizes.
CoRR, 2022

The Curse of Unrolling: Rate of Differentiating Through Optimization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient and Modular Implicit Differentiation.
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.
Proceedings of the International Conference on Machine Learning, 2022

Only tails matter: Average-Case Universality and Robustness in the Convex Regime.
Proceedings of the International Conference on Machine Learning, 2022

GradMax: Growing Neural Networks using Gradient Information.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Super-Acceleration with Cyclical Step-sizes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Bridging the Gap Between Adversarial Robustness and Optimization Bias.
CoRR, 2021

Boosting Variational Inference With Locally Adaptive Step-Sizes.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Average-case Acceleration for Bilinear Games and Normal Matrices.
Proceedings of the 9th International Conference on Learning Representations, 2021

SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality.
Proceedings of the Conference on Learning Theory, 2021

2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.
CoRR, 2020

The Geometry of Sign Gradient Descent.
CoRR, 2020

Average-case Acceleration Through Spectral Density Estimation.
CoRR, 2020

Universal Asymptotic Optimality of Polyak Momentum.
Proceedings of the 37th International Conference on Machine Learning, 2020

Acceleration through spectral density estimation.
Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

On the interplay between noise and curvature and its effect on optimization and generalization.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Linearly Convergent Frank-Wolfe without Line-Search.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
A Test for Shared Patterns in Cross-modal Brain Activation Analysis.
CoRR, 2019

SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python.
CoRR, 2019

The Difficulty of Training Sparse Neural Networks.
CoRR, 2019

Information matrices and generalization.
CoRR, 2019

Proximal Splitting Meets Variance Reduction.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods.
J. Mach. Learn. Res., 2018

Adaptive Three Operator Splitting.
Proceedings of the 35th International Conference on Machine Learning, 2018

Frank-Wolfe with Subsampling Oracle.
Proceedings of the 35th International Conference on Machine Learning, 2018

Frank-Wolfe Splitting via Augmented Lagrangian Method.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
SymPy: symbolic computing in Python.
PeerJ Comput. Sci., 2017

On the Consistency of Ordinal Regression Methods.
J. Mach. Learn. Res., 2017

Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ASAGA: Asynchronous Parallel SAGA.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
SymPy: Symbolic computing in Python.
PeerJ Prepr., 2016

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding.
NeuroImage, 2016

Hyperparameter optimization with approximate gradient.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Scikit-learn: Machine Learning Without Learning the Machinery.
GetMobile Mob. Comput. Commun., 2015

Data-driven HRF estimation for encoding and decoding models.
NeuroImage, 2015

2014
Machine learning for neuroimaging with scikit-learn.
Frontiers Neuroinformatics, 2014

A perceptual-to-conceptual gradient of word coding along the ventral path.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Decoding perceptual thresholds from MEG/EEG.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

Automatic pathology classification using a single feature machine learning support - vector machines.
Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, 2014

2013
API design for machine learning software: experiences from the scikit-learn project.
CoRR, 2013

HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2013

2012
Improved Brain Pattern Recovery through Ranking Approaches.
Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Learning to Rank from Medical Imaging Data.
Proceedings of the Machine Learning in Medical Imaging - Third International Workshop, 2012

2011
Scikit-learn: Machine Learning in Python.
J. Mach. Learn. Res., 2011

Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity.
Proceedings of the Information Processing in Medical Imaging, 2011


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