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ORCIDFabian Pedregosa
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2020 – today
- 2024
[j10]Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa:
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis. Trans. Mach. Learn. Res. 2024 (2024)- [i40]Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan:
Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators. CoRR abs/2402.13984 (2024) - [i39]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:
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR abs/2406.09073 (2024) - [i38]Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa:
Stepping on the Edge: Curvature Aware Learning Rate Tuners. CoRR abs/2407.06183 (2024) - 2023
- [j9]Courtney Paquette, Bart van Merriënboer, Elliot Paquette, Fabian Pedregosa:
Halting Time is Predictable for Large Models: A Universality Property and Average-Case Analysis. Found. Comput. Math. 23(2): 597-673 (2023) - [c31]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. AISTATS 2023: 1703-1718 - [c30]Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. ICML 2023: 169-195 - [i37]Vincent Roulet, Atish Agarwala, Fabian Pedregosa:
On the Interplay Between Stepsize Tuning and Progressive Sharpening. CoRR abs/2312.00209 (2023) - 2022
- [c29]Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa:
Super-Acceleration with Cyclical Step-sizes. AISTATS 2022: 3028-3065 - [c28]Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov:
GradMax: Growing Neural Networks using Gradient Information. ICLR 2022 - [c27]Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette:
Only tails matter: Average-Case Universality and Robustness in the Convex Regime. ICML 2022: 4474-4491 - [c26]Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse:
On Implicit Bias in Overparameterized Bilevel Optimization. ICML 2022: 22234-22259 - [c25]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. NeurIPS 2022 - [c24]Damien Scieur, Gauthier Gidel, Quentin Bertrand, Fabian Pedregosa:
The Curse of Unrolling: Rate of Differentiating Through Optimization. NeurIPS 2022 - [i36]Utku Evci, Max Vladymyrov, Thomas Unterthiner, Bart van Merriënboer, Fabian Pedregosa:
GradMax: Growing Neural Networks using Gradient Information. CoRR abs/2201.05125 (2022) - [i35]Robert M. Gower, Mathieu Blondel, Nidham Gazagnadou, Fabian Pedregosa:
Cutting Some Slack for SGD with Adaptive Polyak Stepsizes. CoRR abs/2202.12328 (2022) - [i34]Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette:
Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime. CoRR abs/2206.09901 (2022) - [i33]Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington:
Second-order regression models exhibit progressive sharpening to the edge of stability. CoRR abs/2210.04860 (2022) - [i32]Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa:
Extragradient with Positive Momentum is Optimal for Games with Cross-Shaped Jacobian Spectrum. CoRR abs/2211.04659 (2022) - [i31]Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare:
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. CoRR abs/2212.04025 (2022) - [i30]Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse:
On Implicit Bias in Overparameterized Bilevel Optimization. CoRR abs/2212.14032 (2022) - 2021
- [c23]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. COLT 2021: 3548-3626 - [c22]Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur:
Average-case Acceleration for Bilinear Games and Normal Matrices. ICLR 2021 - [c21]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. IJCAI 2021: 2337-2343 - [i29]Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette:
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. CoRR abs/2102.04396 (2021) - [i28]Fartash Faghri, Cristina Nader Vasconcelos, David J. Fleet, Fabian Pedregosa, Nicolas Le Roux:
Bridging the Gap Between Adversarial Robustness and Optimization Bias. CoRR abs/2102.08868 (2021) - [i27]Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch:
Boosting Variational Inference With Locally Adaptive Step-Sizes. CoRR abs/2105.09240 (2021) - [i26]Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert:
Efficient and Modular Implicit Differentiation. CoRR abs/2105.15183 (2021) - 2020
- [c20]Fabian Pedregosa, Geoffrey Négiar, Armin Askari, Martin Jaggi:
Linearly Convergent Frank-Wolfe without Line-Search. AISTATS 2020: 1-10 - [c19]Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux:
On the interplay between noise and curvature and its effect on optimization and generalization. AISTATS 2020: 3503-3513 - [c18]Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa:
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. ICML 2020: 7253-7262 - [c17]Fabian Pedregosa, Damien Scieur:
Acceleration through spectral density estimation. ICML 2020: 7553-7562 - [c16]Damien Scieur, Fabian Pedregosa:
Universal Asymptotic Optimality of Polyak Momentum. ICML 2020: 8565-8572 - [i25]Fabian Pedregosa, Damien Scieur:
Average-case Acceleration Through Spectral Density Estimation. CoRR abs/2002.04756 (2020) - [i24]Lukas Balles, Fabian Pedregosa, Nicolas Le Roux:
The Geometry of Sign Gradient Descent. CoRR abs/2002.08056 (2020) - [i23]Geoffrey Négiar, Gideon Dresdner, Alicia Y. Tsai, Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa:
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. CoRR abs/2002.11860 (2020) - [i22]Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur:
Average-case Acceleration for Bilinear Games and Normal Matrices. CoRR abs/2010.02076 (2020)
2010 – 2019
- 2019
- [c15]Fabian Pedregosa, Kilian Fatras, Mattia Casotto:
Proximal Splitting Meets Variance Reduction. AISTATS 2019: 1-10 - [i21]Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux:
Information matrices and generalization. CoRR abs/1906.07774 (2019) - [i20]Utku Evci, Fabian Pedregosa, Aidan N. Gomez, Erich Elsen:
The Difficulty of Training Sparse Neural Networks. CoRR abs/1906.10732 (2019) - [i19]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:
SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python. CoRR abs/1907.10121 (2019) - [i18]Elena Kalinina, Fabian Pedregosa, Vittorio Iacovella, Emanuele Olivetti, Paolo Avesani:
A Test for Shared Patterns in Cross-modal Brain Activation Analysis. CoRR abs/1910.05271 (2019) - 2018
- [j8]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods. J. Mach. Learn. Res. 19: 81:1-81:68 (2018) - [c14]Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien:
Frank-Wolfe Splitting via Augmented Lagrangian Method. AISTATS 2018: 1456-1465 - [c13]Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont:
Frank-Wolfe with Subsampling Oracle. ICML 2018: 2596-2605 - [c12]Fabian Pedregosa, Gauthier Gidel:
Adaptive Three Operator Splitting. ICML 2018: 4082-4091 - [i17]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Improved asynchronous parallel optimization analysis for stochastic incremental methods. CoRR abs/1801.03749 (2018) - [i16]Thomas Kerdreux, Fabian Pedregosa, Alexandre d'Aspremont:
Frank-Wolfe with Subsampling Oracle. CoRR abs/1803.07348 (2018) - [i15]Fabian Pedregosa, Gauthier Gidel:
Adaptive Three Operator Splitting. CoRR abs/1804.02339 (2018) - [i14]Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien:
Frank-Wolfe Splitting via Augmented Lagrangian Method. CoRR abs/1804.03176 (2018) - 2017
- [j7]Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort:
On the Consistency of Ordinal Regression Methods. J. Mach. Learn. Res. 18: 55:1-55:35 (2017) - [j6]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:
SymPy: symbolic computing in Python. PeerJ Comput. Sci. 3: e103 (2017) - [c11]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
ASAGA: Asynchronous Parallel SAGA. AISTATS 2017: 46-54 - [c10]Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. NIPS 2017: 56-65 - [i13]Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien:
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. CoRR abs/1707.06468 (2017) - 2016
- [j5]Valentina Borghesani, Fabian Pedregosa, Marco Buiatti, Alexis Amadon, Evelyn Eger, Manuela Piazza:
Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding. NeuroImage 143: 128-140 (2016) - [c9]Fabian Pedregosa:
Hyperparameter optimization with approximate gradient. ICML 2016: 737-746 - [i12]Fabian Pedregosa:
Hyperparameter optimization with approximate gradient. CoRR abs/1602.02355 (2016) - [i11]Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien:
Asaga: Asynchronous Parallel Saga. CoRR abs/1606.04809 (2016) - [i10]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:
SymPy: Symbolic computing in Python. PeerJ Prepr. 4: e2083 (2016) - 2015
- [j4]Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Bertrand Thirion, Alexandre Gramfort:
Data-driven HRF estimation for encoding and decoding models. NeuroImage 104: 209-220 (2015) - [j3]Gaël Varoquaux, Lars Buitinck, Gilles Louppe, Olivier Grisel, Fabian Pedregosa, Andreas Mueller:
Scikit-learn: Machine Learning Without Learning the Machinery. GetMobile Mob. Comput. Commun. 19(1): 29-33 (2015) - 2014
- [j2]Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gaël Varoquaux:
Machine learning for neuroimaging with scikit-learn. Frontiers Neuroinformatics 8: 14 (2014) - [c8]Fernando Yepes-Calderon, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, Natasha Leporé:
Automatic pathology classification using a single feature machine learning support - vector machines. Medical Imaging: Computer-Aided Diagnosis 2014: 903524 - [c7]Yousra Bekhti, Nicolas Zilber, Fabian Pedregosa, Philippe Ciuciu, Virginie van Wassenhove, Alexandre Gramfort:
Decoding perceptual thresholds from MEG/EEG. PRNI 2014: 1-4 - [c6]Valentina Borghesani, Fabian Pedregosa, Evelyn Eger, Marco Buiatti, Manuela Piazza:
A perceptual-to-conceptual gradient of word coding along the ventral path. PRNI 2014: 1-4 - [i9]Fabian Pedregosa, Michael Eickenberg, Philippe Ciuciu, Alexandre Gramfort, Bertrand Thirion:
Data-driven HRF estimation for encoding and decoding models. CoRR abs/1402.7015 (2014) - [i8]Fabian Pedregosa, Francis R. Bach, Alexandre Gramfort:
On the Consistency of Ordinal Regression Methods. CoRR abs/1408.2327 (2014) - [i7]Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, Gaël Varoquaux:
Machine Learning for Neuroimaging with Scikit-Learn. CoRR abs/1412.3919 (2014) - 2013
- [c5]Michael Eickenberg, Mehdi Senoussi, Fabian Pedregosa, Alexandre Gramfort, Bertrand Thirion:
Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures. PRNI 2013: 5-8 - [c4]Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort:
HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models. PRNI 2013: 165-169 - [i6]Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort:
HRF estimation improves sensitivity of fMRI encoding and decoding models. CoRR abs/1305.2788 (2013) - [i5]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:
API design for machine learning software: experiences from the scikit-learn project. CoRR abs/1309.0238 (2013) - [i4]Michael Eickenberg, Fabian Pedregosa, Senoussi Mehdi, Alexandre Gramfort, Bertrand Thirion:
Second order scattering descriptors predict fMRI activity due to visual textures. CoRR abs/1310.1257 (2013) - 2012
- [c3]Fabian Pedregosa, Elodie Cauvet, Gaël Varoquaux, Christophe Pallier, Bertrand Thirion, Alexandre Gramfort:
Learning to Rank from Medical Imaging Data. MLMI 2012: 234-241 - [c2]Fabian Pedregosa, Elodie Cauvet, Gaël Varoquaux, Christophe Pallier, Bertrand Thirion, Alexandre Gramfort:
Improved Brain Pattern Recovery through Ranking Approaches. PRNI 2012: 9-12 - [i3]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:
Scikit-learn: Machine Learning in Python. CoRR abs/1201.0490 (2012) - [i2]Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux, Bertrand Thirion, Christophe Pallier, Elodie Cauvet:
Improved brain pattern recovery through ranking approaches. CoRR abs/1207.3520 (2012) - [i1]Fabian Pedregosa, Alexandre Gramfort, Gaël Varoquaux, Elodie Cauvet, Christophe Pallier, Bertrand Thirion:
Learning to rank from medical imaging data. CoRR abs/1207.3598 (2012) - 2011
- [j1]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:
Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12: 2825-2830 (2011) - [c1]Gaël Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, Bertrand Thirion:
Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity. IPMI 2011: 562-573
Coauthor Index
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last updated on 2024-10-04 20:03 CEST by the dblp team
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