no code implementations • 23 May 2018 • Matthew M. Dunlop, Dejan Slepčev, Andrew M. Stuart, Matthew Thorpe
Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised learning; in particular the extension, to this graph setting, of the probit algorithm, level set and kriging methods, are studied.
no code implementations • 9 Mar 2018 • Victor Chen, Matthew M. Dunlop, Omiros Papaspiliopoulos, Andrew M. Stuart
One popular formulation of such problems is as Bayesian inverse problems, where a prior distribution is used to regularize inference on a high-dimensional latent state, typically a function or a field.