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 …

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

We consider the problem of optimizing hybrid structures (mixture of discrete and continuous
input variables) via expensive black-box function evaluations. This problem arises in many …

Bayesian optimization (BO) is an efficient framework for solving black-box optimization
problems with expensive function evaluations. This paper addresses the BO problem setting …

Optimizing expensive to evaluate black-box functions over an input space consisting of all
permutations of d objects is an important problem with many real-world applications. For …

One method to solve expensive black-box optimization problems is to use a surrogate model
that approximates the objective based on previous observed evaluations. The surrogate …

We consider the problem of constrained multi-objective blackbox optimization using
expensive function evaluations, where the goal is to approximate the true Pareto set of …

Scientists and engineers in diverse domains need to perform expensive experiments to
optimize combinatorial spaces, where each candidate input is a discrete structure (eg …

We consider the problem of constrained multi-objective (MO) blackbox optimization using
expensive function evaluations, where the goal is to approximate the true Pareto set of …

Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal
data errors, and provide deeper insight into the data. Inference query explanation seeks to …