59 citations · 195 across the 37 of their papers we have counts for
5 papers · 1 filter
Optimization Insights into Deep Diagonal Linear Networks
Hippolyte Labarrière, Cesare Molinari, Lorenzo Rosasco +2
Gradient-based methods successfully train highly overparameterized models in practice, even though the associated optimization problems are markedly nonconvex. Understanding the me…
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…
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
Marco Rando, Luigi Carratino, Silvia Villa +1
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 continuo…
Learning Multiple Visual Tasks while Discovering their Structure
Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa
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, c…
PADDLE: Proximal Algorithm for Dual Dictionaries LEarning
Curzio Basso, Matteo Santoro, Alessandro Verri +1
Recently, considerable research efforts have been devoted to the design of methods to learn from data overcomplete dictionaries for sparse coding. However, learned dictionaries req…