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Found 13 papers, 1 papers with code
Variance reduction techniques for stochastic proximal point algorithms
Stochastic proximal point algorithms have been studied as an alternative to stochastic gradient algorithms since they are more stable with respect to the choice of the stepsize but a proper variance reduced version is missing.
Snacks: a fast large-scale kernel SVM solver
Kernel methods provide a powerful framework for non parametric learning.
Iterative regularization in classification via hinge loss diagonal descent
In this paper, we focus on iterative regularization in the context of classification.
Stochastic Zeroth order Descent with Structured Directions
For smooth convex functions we prove almost sure convergence of the iterates and a convergence rate on the function values of the form $O(d/l k^{-c})$ for every $c<1/2$, which is arbitrarily close to the one of Stochastic Gradient Descent (SGD) in terms of number of iterations.
Iterative regularization for low complexity regularizers
Our approach is based on a primal-dual algorithm of which we analyze convergence and stability properties, even in the case where the original problem is unfeasible.
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
In this paper, we introduce Ada-BKB (Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process optimization algorithm for functions on continuous domains, that provably runs in $O(T^2 d_\text{eff}^2)$, where $d_\text{eff}$ is the effective dimension of the explored space, and which is typically much smaller than $T$.
Iterative regularization for convex regularizers
We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex.
Don't relax: early stopping for convex regularization
We consider the problem of designing efficient regularization algorithms when regularization is encoded by a (strongly) convex functional.
Convergence of the Forward-Backward Algorithm: Beyond the Worst Case with the Help of Geometry
We provide a comprehensive study of the convergence of the forward-backward algorithm under suitable geometric conditions, such as conditioning or {\L}ojasiewicz properties.
Learning Multiple Visual Tasks while Discovering their Structure
Multi-task learning is a natural approach for computer vision applications that require the simultaneous solution of several distinct but related problems, e. g. object detection, classification, tracking of multiple agents, or denoising, to name a few.
Learning with incremental iterative regularization
Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method.
On Learnability, Complexity and Stability
We consider the fundamental question of learnability of a hypotheses class in the supervised learning setting and in the general learning setting introduced by Vladimir Vapnik.
A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups
We deal with the problem of variable selection when variables must be selected group-wise, with possibly overlapping groups defined a priori.
