Fabian Pedregosa
Fabian Pedregosa
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TitleCited byYear
Scikit-learn: Machine learning in Python
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, ...
Journal of machine learning research 12 (Oct), 2825-2830, 2011
API design for machine learning software: experiences from the scikit-learn project
L Buitinck, G Louppe, M Blondel, F Pedregosa, A Mueller, O Grisel, ...
arXiv preprint arXiv:1309.0238, 2013
Machine learning for neuroimaging with scikit-learn
A Abraham, F Pedregosa, M Eickenberg, P Gervais, A Mueller, J Kossaifi, ...
Frontiers in neuroinformatics 8, 14, 2014
SymPy: symbolic computing in Python
A Meurer, CP Smith, M Paprocki, O Čertík, SB Kirpichev, M Rocklin, ...
PeerJ Computer Science 3, e103, 2017
Multi-subject dictionary learning to segment an atlas of brain spontaneous activity
G Varoquaux, A Gramfort, F Pedregosa, V Michel, B Thirion
Biennial International Conference on Information Processing in Medical …, 2011
ASAGA: Asynchronous parallel SAGA
R Leblond, F Pedregosa, S Lacoste-Julien
Proceedings of the 20th International Conference on Artificial Intelligence …, 2017
Hyperparameter optimization with approximate gradient
F Pedregosa
Proceedings of the 33nd International Conference on Machine Learning, ICML …, 2016
Data-driven HRF estimation for encoding and decoding models
F Pedregosa, M Eickenberg, P Ciuciu, B Thirion, A Gramfort
NeuroImage 104, 209-220, 2015
Scikit-learn: Machine learning without learning the machinery
G Varoquaux, L Buitinck, G Louppe, O Grisel, F Pedregosa, A Mueller
GetMobile: Mobile Computing and Communications 19 (1), 29-33, 2015
Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding
V Borghesani, F Pedregosa, M Buiatti, A Amadon, E Eger, M Piazza
NeuroImage 143, 128-140, 2016
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
F Pedregosa, R Leblond, S Lacoste-Julien
Advances in Neural Information Processing Systems 30, 2017
Feature extraction and supervised learning on fMRI: from practice to theory
F Pedregosa-Izquierdo
Université Pierre et Marie Curie-Paris VI, 2015
On the consistency of ordinal regression methods
F Pedregosa, F Bach, A Gramfort
Journal of Machine Learning Research 18, 1-35, 2017
Improved asynchronous parallel optimization analysis for stochastic incremental methods
R Leblond, F Pedregosa, S Lacoste-Julien
Journal of Machine Learning Research 19 (81), 2018
Learning to rank from medical imaging data
F Pedregosa, E Cauvet, G Varoquaux, C Pallier, B Thirion, A Gramfort
Machine Learning in Medical Imaging, 234-241, 2012
Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning
SM Kia, F Pedregosa, A Blumenthal, A Passerini
Journal of neuroscience methods 285, 97-108, 2017
Frank-Wolfe with Subsampling Oracle
T Kerdreux, F Pedregosa, A d'Aspremont
Proceedings of the 35th International Conference on Machine Learning 80 …, 2018
Frank-Wolfe Splitting via Augmented Lagrangian Method
G Gidel, F Pedregosa, S Lacoste-Julien
Proceedings of the Twenty-First International Conference on Artificial …, 2018
Adaptive Three Operator Splitting
F Pedregosa, G Gidel
Proceedings of the 35th International Conference on Machine Learning 35, 2018
Scipy lecture notes
G Varoquaux, E Gouillart, O Vahtras, V Haenel, NP Rougier, R Gommers, ...
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