KU Leuven Seminars on Optimization in Engineering - Francesco Orabona
Start date: 19/06/2012
Location: ESAT 00.62
"Efficient Stochastic and Batch Optimization Algorithms"
Francesco Orabona (TTI Chicago)
Many machine learning problems reduce to solving a convex
optimization problem. The choice of the correct optimization algorithm
is often critical to obtain a good solution in a reasonable amount of
time, and it depends on the characteristics of the objective function.
I present a stochastic and a batch algorithm for efficiently solving
strongly convex functions and composite ones with Lipschitz parts
respectively. I also show how the optimization algorithm can guide the
design of the objective function, to achieve good performance and fast
convergence rates. Experimental results will be shown in Multi Kernel
Learning, Matrix Completion and Robust PCA applications.