Upcoming MAMS Colloquium Series

Fall 2026

10/9/2026, Friday 3:15-4:15 pm in A.W. Smith 349
Speaker: Professor Hwanwoo Kim, UT Dallas
Title: Efficient Gaussian Process Surrogates for Blackbox Optimization and Posterior Approximation
Abstract: In this talk, we explore efficient Gaussian process surrogate modeling in two distinct contexts: bandit optimization and blackbox posterior approximation. For optimization, we propose novel noise-free Bayesian optimization strategies that incorporate a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. For blackbox posterior approximation, we propose utilizing optimization iterates as design points to build a Gaussian process surrogate model for the unnormalized log-posterior density when the likelihood is intractable. We show that the Hellinger distance between the true and approximate posterior distributions decays at a near-optimal rate. We demonstrate the effectiveness of our optimization algorithms on benchmark non-convex test functions and in a black-box engineering design problem, and showcase our posterior approximation approach in Bayesian inference for parameters of dynamical systems.