59 citations · 195 across the 37 of their papers we have counts for
6 papers · 1 filter
On Learning the Optimal Regularization Parameter in Inverse Problems
Jonathan Chirinos Rodriguez, Ernesto De Vito, Cesare Molinari +2
Selecting the best regularization parameter in inverse problems is a classical and yet challenging problem. Recently, data-driven approaches have become popular to tackle this chal…
Variance reduction techniques for stochastic proximal point algorithms
Cheik Traoré, Vassilis Apidopoulos, Saverio Salzo +1
In the context of finite sums minimization, variance reduction techniques are widely used to improve the performance of state-of-the-art stochastic gradient methods. Their practica…
Regularization properties of dual subgradient flow
Vassilis Apidopoulos, Cesare Molinari, Lorenzo Rosasco +1
Dual gradient descent combined with early stopping represents an efficient alternative to the Tikhonov variational approach when the regularizer is strongly convex. However, for ma…
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
Marco Rando, Cesare Molinari, Lorenzo Rosasco +1
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…
On the convergence of proximal gradient methods for convex simple bilevel optimization
Puya Latafat, Andreas Themelis, Silvia Villa +1
This paper studies proximal gradient iterations for solving simple bilevel optimization problems where both the upper and the lower level cost functions are split as the sum of dif…
Snacks: a fast large-scale kernel SVM solver
Sofiane Tanji, Andrea Della Vecchia, François Glineur +1
Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear stat…