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 (BO) is a prominent approach to optimizing expensive-to-evaluate
black-box functions. The massive computational capability of edge devices such as mobile …

Nanoporous materials (NPMs) could be used to store, capture, and sense many different
gases. Given an adsorption task, we often wish to search a library of NPMs for the one with …

Many real-world problems can be phrased as a multi-objective optimization problem, where
the goal is to identify the best set of compromises between the competing objectives. Multi …

How can we efficiently gather information to optimize an unknown function, when presented
with multiple, mutually dependent information sources with different costs? For example …

We consider the problem of optimizing combinatorial spaces (eg, sequences, trees, and
graphs) using expensive black-box function evaluations. For example, optimizing molecules …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …

In a standard setting of Bayesian optimization (BO), the objective function evaluation is
assumed to be highly expensive. Multi-fidelity Bayesian optimization (MFBO) accelerates BO …

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its
objective function, is popular for black-box optimization. However, due to the limitations of …

Abstract Information-theoretic Bayesian optimization techniques have become popular for
optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities …