Marco Rando — accepted AI-conference papers

2023

An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization

Marco Rando, Cesare Molinari, Lorenzo Rosasco, Silvia Villa

NeurIPS 2023poster

Finite-difference methods are a class of algorithms designed to solve black-box optimization problems by approximating a gradient of the target function on a set of directions. In black-box optimization, the non-smooth setting is particularly relevant since, in practice, differentiability and smooth…

Cited by 18 Source ↗PDF ↗Scholar ↗

2022

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization

Marco Rando, Luigi Carratino, Silvia Villa, Lorenzo Rosasco

AISTATS 2022poster

Gaussian process optimization is a successful class of algorithms(e.g. GP-UCB) to optimize a black-box function through sequential evaluations. However, for functions with continuous domains, Gaussian process optimization has to rely on either a fixed discretization of the space, or the solution of…

Cited by 6 Source ↗PDF ↗Scholar ↗