We present a general kernel-based framework for learning operators betwe...
In recent years, Bayesian inference in large-scale inverse problems foun...
This article presents a general approximation-theoretic framework to ana...
This article presents a three-step framework for learning and solving pa...
This paper presents a variational representation of the Bayes' law using...
Graph-based semi-supervised regression (SSR) is the problem of estimatin...
We present a new approach for sampling conditional measures that enables...
We develop a general framework for data-driven approximation of input-ou...
Graph Laplacians computed from weighted adjacency matrices are widely us...
Graph-based semi-supervised learning is the problem of propagating label...
We analyze the spectral clustering procedure for identifying coarse stru...
We study a Metropolis-Hastings algorithm for target measures that are
ab...
We present a cost-effective method for model calibration and solution of...
We introduce a new class of Metropolis-Hastings algorithms for sampling
...