Pedregosa, Fabian

37 publications

TMLR 2025 How Far Away Are Truly Hyperparameter-Free Learning Algorithms? Priya Kasimbeg, Vincent Roulet, Naman Agarwal, Sourabh Medapati, Fabian Pedregosa, Atish Agarwala, George E. Dahl

TMLR 2025 Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan

NeurIPS 2024 Stepping on the Edge: Curvature Aware Learning Rate Tuners Vincent Roulet, Atish Agarwala, Jean-Bastien Grill, Grzegorz Swirszcz, Mathieu Blondel, Fabian Pedregosa

NeurIPSW 2024 Unlearning In- vs. Out-of-Distribution Data in LLMs Under Gradient-Based Methods Teodora Baluta, Pascal Lamblin, Daniel Tarlow, Fabian Pedregosa, Gintare Karolina Dziugaite

TMLR 2024 When Is Momentum Extragradient Optimal? a Polynomial-Based Analysis Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

AISTATS 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

NeurIPSW 2023 On the Interplay Between Stepsize Tuning and Progressive Sharpening Vincent Roulet, Atish Agarwala, Fabian Pedregosa

ICML 2023 Second-Order Regression Models Exhibit Progressive Sharpening to the Edge of Stability Atish Agarwala, Fabian Pedregosa, Jeffrey Pennington

AISTATS 2022 Super-Acceleration with Cyclical Step-Sizes Baptiste Goujaud, Damien Scieur, Aymeric Dieuleveut, Adrien B. Taylor, Fabian Pedregosa

NeurIPSW 2022 A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G Bellemare

NeurIPSW 2022 A Second-Order Regression Model Shows Edge of Stability Behavior Fabian Pedregosa, Atish Agarwala, Jeffrey Pennington

NeurIPS 2022 Efficient and Modular Implicit Differentiation Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-Lopez, Fabian Pedregosa, Jean-Philippe Vert

ICLR 2022 GradMax: Growing Neural Networks Using Gradient Information Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov

NeurIPSW 2022 Momentum Extragradient Is Optimal for Games with Cross-Shaped Spectrum Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa

ICML 2022 On Implicit Bias in Overparameterized Bilevel Optimization Paul Vicol, Jonathan P Lorraine, Fabian Pedregosa, David Duvenaud, Roger B Grosse

ICML 2022 Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime Leonardo Cunha, Gauthier Gidel, Fabian Pedregosa, Damien Scieur, Courtney Paquette

NeurIPS 2022 The Curse of Unrolling: Rate of Differentiating Through Optimization Damien Scieur, Gauthier Gidel, Quentin Bertrand, Fabian Pedregosa

ICLR 2021 Average-Case Acceleration for Bilinear Games and Normal Matrices Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

IJCAI 2021 Boosting Variational Inference with Locally Adaptive Step-Sizes Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch

COLT 2021 SGD in the Large: Average-Case Analysis, Asymptotics, and Stepsize Criticality Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette

ICML 2020 Acceleration Through Spectral Density Estimation Fabian Pedregosa, Damien Scieur

AISTATS 2020 Linearly Convergent Frank-Wolfe with Backtracking Line-Search Fabian Pedregosa, Geoffrey Negiar, Armin Askari, Martin Jaggi

AISTATS 2020 On the Interplay Between Noise and Curvature and Its Effect on Optimization and Generalization Valentin Thomas, Fabian Pedregosa, Bart Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio, Nicolas Le Roux

ICML 2020 Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert Freund, Fabian Pedregosa

ICLR 2020 The Geometry of Sign Gradient Descent Lukas Balles, Fabian Pedregosa, Nicolas Le Roux

ICML 2020 Universal Average-Case Optimality of Polyak Momentum Damien Scieur, Fabian Pedregosa

AISTATS 2019 Proximal Splitting Meets Variance Reduction Fabian Pedregosa, Kilian Fatras, Mattia Casotto

ICMLW 2019 The Difficulty of Training Sparse Neural Networks Utku Evci, Fabian Pedregosa, Aidan Gomez, Erich Elsen

ICML 2018 Adaptive Three Operator Splitting Fabian Pedregosa, Gauthier Gidel

AISTATS 2018 Frank-Wolfe Splitting via Augmented Lagrangian Method Gauthier Gidel, Fabian Pedregosa, Simon Lacoste-Julien

ICML 2018 Frank-Wolfe with Subsampling Oracle Thomas Kerdreux, Fabian Pedregosa, Alexandre d’Aspremont

JMLR 2018 Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods Remi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

AISTATS 2017 ASAGA: Asynchronous Parallel SAGA Rémi Leblond, Fabian Pedregosa, Simon Lacoste-Julien

NeurIPS 2017 Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization Fabian Pedregosa, Rémi Leblond, Simon Lacoste-Julien

JMLR 2017 On the Consistency of Ordinal Regression Methods Fabian Pedregosa, Francis Bach, Alexandre Gramfort

ICML 2016 Hyperparameter Optimization with Approximate Gradient Fabian Pedregosa

MLOSS 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, Édouard Duchesnay