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08/18/2023
Variance reduction techniques for stochastic proximal point algorithms
In the context of finite sums minimization, variance reduction technique...
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04/17/2023
Snacks: a fast large-scale kernel SVM solver
Kernel methods provide a powerful framework for non parametric learning....
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06/10/2022
Stochastic Zeroth order Descent with Structured Directions
We introduce and analyze Structured Stochastic Zeroth order Descent (S-S...
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02/01/2022
Iterative regularization for low complexity regularizers
Iterative regularization exploits the implicit bias of an optimization a...
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01/14/2022
Convergence of an Asynchronous Block-Coordinate Forward-Backward Algorithm for Convex Composite Optimization
In this paper, we study the convergence properties of a randomized block...
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06/16/2021
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization
Gaussian process optimization is a successful class of algorithms (e.g. ...
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06/17/2020
Implicit regularization for convex regularizers
We study implicit regularization for over-parameterized linear models, w...
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03/28/2017
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry
We provide a comprehensive study of the convergence of forward-backward ...
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04/13/2015
Learning Multiple Visual Tasks while Discovering their Structure
Multi-task learning is a natural approach for computer vision applicatio...
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04/30/2014
Learning with incremental iterative regularization
Within a statistical learning setting, we propose and study an iterative...
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03/24/2013
On Learnability, Complexity and Stability
We consider the fundamental question of learnability of a hypotheses cla...
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09/03/2012
Proximal methods for the latent group lasso penalty
We consider a regularized least squares problem, with regularization by ...
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08/13/2012
Nonparametric sparsity and regularization
In this work we are interested in the problems of supervised learning an...
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11/16/2010