Speaker: Dejan Slepcev

Abstract: We consider a regression problem of semi-supervised learning: given real-valued labels on a small subset of data recover the function on the whole data set while taking into account the information provided by a large number of unlabeled data points. Objective functionals modeling this regression problem involve terms rewarding the regularity of the function estimate while enforcing agreement with the labels provided. We will discuss regularizations motivated by p-Laplace equation. We will discuss and prove which of these functionals make sense when the number of data points goes to infinity. The talk is based on joint work with Matthew Thorpe (arXiv:1707.06213).