10 citations · 10 across the 5 of their papers we have counts for
5 papers
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
Hwanwoo Kim, Daniel Sanz-Alonso
This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new a…
Optimization on Manifolds via Graph Gaussian Processes
Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang
This paper integrates manifold learning techniques within a \emph{Gaussian process upper confidence bound} algorithm to optimize an objective function on a manifold. Our approach i…
Hierarchical Ensemble Kalman Methods with Sparsity-Promoting Generalized Gamma Hyperpriors
Hwanwoo Kim, Daniel Sanz-Alonso, Alexander Strang
This paper introduces a computational framework to incorporate flexible regularization techniques in ensemble Kalman methods for nonlinear inverse problems. The proposed methodolog…
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
Shiv Agrawal, Hwanwoo Kim, Daniel Sanz-Alonso +1
Hierarchical models with gamma hyperpriors provide a flexible, sparse-promoting framework to bridge and regularizations in Bayesian formulations to inverse problems. De…
Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries
John Harlim, Shixiao Jiang, Hwanwoo Kim +1
This paper develops manifold learning techniques for the numerical solution of PDE-constrained Bayesian inverse problems on manifolds with boundaries. We introduce graphical Matérn…