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 …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

We design and analyse variations of the classical Thompson sampling (TS) procedure for
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …

The popularity of Bayesian optimization methods for efficient exploration of parameter
spaces has lead to a series of papers applying Gaussian processes as surrogates in the …

Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in
computing power increasingly rely on parallelization rather than faster processors. This …

Abstract The Multi-points Expected Improvement criterion (or q-EI) has recently been studied
in batch-sequential Bayesian Optimization. This paper deals with a new way of computing q …

This book is the first ever to focus on the emerging field of self-aware computing from an
engineering perspective. It first comprehensively introduces fundamentals for self …

CityCOVID is a detailed agent-based model that represents the behaviors and social
interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 …

The discovery of therapeutic molecules is fundamentally a multi-objective optimization
problem. One formulation of the problem is to identify molecules that simultaneously exhibit …

We consider parallel global optimization of derivative-free expensive-to-evaluate functions,
and propose an efficient method based on stochastic approximation for implementing a …