Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

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 …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

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 …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

The accurate and reliable prediction of properties of molecules typically requires
computationally intensive quantum-chemical calculations. Recently, machine learning …

We introduce a novel formulation of motion planning, for continuous-time trajectories, as
probabilistic inference. We first show how smooth continuous-time trajectories can be …

We introduce Bayesian optimization, a technique developed for optimizing time-consuming
engineering simulations and for fitting machine learning models on large datasets. Bayesian …