Model Selection and Estimation with Multiple Reproducing Kernel Hilbert Spaces

GUEST LECTURER
Ming Yuan

AFFILIATION
ISyE

ABSTRACT
In this talk, we consider the problem of learning a target function that
belongs to the linear span of a large number of reproducing kernel Hilbert
spaces. Such a problem arises naturally in many practice situations with
the ANOVA, the additive model and multiple kernel learning as the most
well known and important examples. We investigate approaches based on
l1-type complexity regularization and the nonnegative garrote
respectively. We show that the computation of both procedures can be done
efficiently and the nonnegative garrote could be more favorable at times.
We also study their theoretical properties from both variable selection
and estimation perspective. We establish several probabilistic
inequalities providing bounds on the excess risk and L2-error that depend
on the sparsity of the problem.

(parts of the talk are based on joint work with Yi Lin and Vladimir
Koltchinskii respectively.)

DATE & TIME
Tuesday, March 25, 2008 -- 11:00 AM

DURATION
1 hour

LOCATION
Executive Room