59 citations · 195 across the 37 of their papers we have counts for
5 papers · 1 filter
Iterative regularization in classification via hinge loss diagonal descent
Vassilis Apidopoulos, Tomaso Poggio, Lorenzo Rosasco +1
Iterative regularization is a classic idea in regularization theory, that has recently become popular in machine learning. On the one hand, it allows to design efficient algorithms…
Stochastic Zeroth order Descent with Structured Directions
Marco Rando, Cesare Molinari, Silvia Villa +1
We introduce and analyze Structured Stochastic Zeroth order Descent (S-SZD), a finite difference approach that approximates a stochastic gradient on a set of orthogonal d…
Fast iterative regularization by reusing data
Cristian Vega, Cesare Molinari, Lorenzo Rosasco +1
Discrete inverse problems correspond to solving a system of equations in a stable way with respect to noise in the data. A typical approach to enforce uniqueness and select a meani…
Iterative regularization for low complexity regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco +1
Iterative regularization exploits the implicit bias of an optimization algorithm to regularize ill-posed problems. Constructing algorithms with such built-in regularization mechani…
Convergence of an Asynchronous Block-Coordinate Forward-Backward Algorithm for Convex Composite Optimization
Cheik Traoré, Saverio Salzo, Silvia Villa
In this paper, we study the convergence properties of a randomized block-coordinate descent algorithm for the minimization of a composite convex objective function, where the block…