6 citations · 6 across the 2 of their papers we have counts for
5 papers
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
Emanuele Frandi, Ricardo Nanculef, Stefano Lodi +2
Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning. T…
A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification
Emanuele Frandi, Ricardo Nanculef, Johan A. K. Suykens
Frank-Wolfe algorithms have recently regained the attention of the Machine Learning community. Their solid theoretical properties and sparsity guarantees make them a suitable choic…
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning
Emanuele Frandi, Ricardo Nanculef, Johan Suykens
Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a s…
A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale SVM Training
Hector Allende, Emanuele Frandi, Ricardo Nanculef +1
Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-W…
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Emanuele Frandi, Ricardo Nanculef, Maria Grazia Gasparo +2
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale…