A L1 representer theorem of vector-valued learning
Shayan Aziznejad, EPFL STI LIB

Meeting • 17 July 2018

Abstract
In this talk, we present a theoretical study on the problem of learning a vector-valued function using generalized TV regularization. We propose a representer theorem that describes the solution set for this problem. Our representer theorem is based on the notion of non-uniform vector-valued L-splines which we introduce and study. At the end, we mention several applications of our representer theorem that can be used.