5 papers
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito +1
In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the in…
The Nyström method for convex loss functions
Andrea Della Vecchia, Ernesto De Vito, Jaouad Mourtada +1
We investigate an extension of classical empirical risk minimization, where the hypothesis space consists of a random subspace within a given Hilbert space. Specifically, we examin…
Q-learning with temporal memory to navigate turbulence
Marco Rando, Martin James, Alessandro Verri +2
We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior inf…
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…