STOR Colloquium: Ming Yuan (Georgia Institute of Technology)

Sparsity in Multiple Kernel Learning

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. A couple of regularization techniques to exploit the sparse nature of the problem will be investigated. The optimality and adaptivity of the these methods will be assessed through oracle type inequalities providing bounds on the excess risk of the resulting prediction rule.

Refreshments will be served at 3:30pm in the 3rd floor lobby of Hanes Hall