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 (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …

We consider the problem of optimizing a black-box function based on noisy bandit feedback.
Kernelized bandit algorithms have shown strong empirical and theoretical performance for …

Wireless cellular networks have many parameters that are normally tuned upon deployment
and re-tuned as the network changes. Many operational parameters affect reference signal …

Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

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 …

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent
method for optimizing complicated black-box reward functions. Various BO algorithms have …

Many black-box optimization tasks arising in high-stakes applications require risk-averse
decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the …

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