Fabian Pedregosa
27 papers · 2011–2024 · 7 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (7) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏃 Academic Marathon (13)
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Conference Polyglot
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Conferences
ICML (9)
AISTATS (7)
NIPS (4)
JMLR (3)
ICLR (2)
COLT (1)
IJCAI (1)
Top co-authors
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