Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

Bayesian optimization is a methodology for optimizing expensive objective functions that
has proven success in the sciences, engineering, and beyond. This timely text provides a …

Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate
objective functions. Classical BO methods assume that the objective function is a black box …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …

We consider Bayesian optimization of the output of a network of functions, where each
function takes as input the output of its parent nodes, and where the network takes …

We consider Bayesian optimization of objective functions of the form $\rho [F (x, W)] $, where
$ F $ is a black-box expensive-to-evaluate function and $\rho $ denotes either the VaR or …

Abstract Value-at-risk (VaR) is an established measure to assess risks in critical real-world
applications with random environmental factors. This paper presents a novel VaR upper …

In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find
an optimal configuration. In many practical problems of interest, one would like to optimize …

In distributionally robust Bayesian optimization (DRBO), an exact computation of the worst-
case expected value requires solving an expensive convex optimization problem. We …

Numerous challenges in science and engineering can be framed as optimization tasks,
including the maximization of reaction yields, the optimization of molecular and materials …