Graph methods for semi-supervised learning and Bayesian inverse problems
Speaker: Daniel Sanz-Alonso, University of Chicago
Abstract: In this talk I will consider two graph-based learning problems. The first one concerns a graph formulation of Bayesian semi-supervised learning, and the second one concerns kernel discretization of Bayesian inverse problems in manifolds. I will show that understanding the continuum limit of these graph-based learning problems is helpful in designing sampling algorithms whose rate of convergence does not deteriorate in the limit of large number of graph nodes.
Room Reservation Information
Room Number: 114 McAllister
Time: 12:20pm - 1:30pm