← Authors

Fabian Pedregosa

27 papers · 2011–2024 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (10) πŸ—ΊοΈ Taxonomy Completionist (28) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (13) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (18) πŸ’Ž Century Club (27) πŸ—ƒοΈ Keyword Collector (106) πŸ“ˆ Trend Setter ⚑ Prolific Year (6) πŸ”₯ Unstoppable (9)

Conferences

ICML (9) AISTATS (7) NIPS (4) JMLR (3) ICLR (2) COLT (1) IJCAI (1)

Top co-authors

Damien Scieur (6) Gauthier Gidel (4) Simon Lacoste-Julien (4) RΓ©mi Leblond (3) Mathieu Blondel (3) Atish Agarwala (2) Alexandre Gramfort (2) Courtney Paquette (2) Francesco Locatello (2) Gideon Dresdner (2)

Keywords

convergence rate (5) stochastic gradient descent (5) gradient descent (4) proximal operator (3) hyperparameter optimization (3) convex optimization (3) quadratic objective (2) frank-wolfe algorithm (2) average-case analysis (2) logistic regression (2) linear convergence (2) gradient-based optimization (2) incremental gradient (2) asynchronous parallel (2) constrained optimization (2) conditional gradient (2) variance reduction (2) empirical risk minimization (2) momentum method (2) multi-core system (2)

Papers

Stepping on the Edge: Curvature Aware Learning Rate Tuners NIPS 2024
Second-order regression models exhibit progressive sharpening to the edge of stability ICML 2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023
Efficient and Modular Implicit Differentiation NIPS 2022
Only tails matter: Average-Case Universality and Robustness in the Convex Regime ICML 2022
Super-Acceleration with Cyclical Step-sizes AISTATS 2022
On Implicit Bias in Overparameterized Bilevel Optimization ICML 2022
The Curse of Unrolling: Rate of Differentiating Through Optimization NIPS 2022
GradMax: Growing Neural Networks using Gradient Information ICLR 2022
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality COLT 2021
Boosting Variational Inference With Locally Adaptive Step-Sizes IJCAI 2021
Average-case Acceleration for Bilinear Games and Normal Matrices ICLR 2021
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization ICML 2020
Universal Average-Case Optimality of Polyak Momentum ICML 2020
Linearly Convergent Frank-Wolfe with Backtracking Line-Search AISTATS 2020
On the interplay between noise and curvature and its effect on optimization and generalization AISTATS 2020
Acceleration through spectral density estimation ICML 2020
Proximal Splitting Meets Variance Reduction AISTATS 2019
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods JMLR 2018
Frank-Wolfe Splitting via Augmented Lagrangian Method AISTATS 2018
Frank-Wolfe with Subsampling Oracle ICML 2018
Adaptive Three Operator Splitting ICML 2018
ASAGA: Asynchronous Parallel SAGA AISTATS 2017
On the Consistency of Ordinal Regression Methods JMLR 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization NIPS 2017
Hyperparameter optimization with approximate gradient ICML 2016
Scikit-learn: Machine Learning in Python JMLR 2011