Bayesian optimization is a sample efficient sequential global optimization method for black-
box, expensive and multi-extremal functions. It generates, and keeps updated, a …

The level set estimation problem seeks to find all points in a domain ${\cal X} $ where the
value of an unknown function $ f:{\cal X}\rightarrow\mathbb {R} $ exceeds a threshold …

Requirements driven search-based testing (also known as falsification) has proven to be a
practical and effective method for discovering erroneous behaviors in Cyber-Physical …

Consider the problem of optimizing a black-box function under the assumption that the
function is Holder smooth and has bounded norm in the reproducing kernel Hilbert space …

We aim to optimize a black-box function $ f:\mathcal {X}\mapsto\mathbb {R} $ under the
assumption that $ f $ is H\" older smooth and has bounded norm in the RKHS associated …

Abstract Level Set Estimation (LSE) is an important problem with applications in various
fields such as material design, biotechnology, machine operational testing, etc. Existing …

Testing under what conditions a product satisfies the desired properties is a fundamental
problem in manufacturing industry. If the condition and the property are respectively …

In material characterization, identifying defective areas on a material surface is fundamental.
The conventional approach involves measuring the relevant physical properties point-by …

We consider a feasibility determination problem via simulation with stochastic binary
outcomes, in which the design space can be either discrete or continuous, and outcomes …

Probabilistic branch and bound (PBnB) is a partition-based algorithm developed for level set
approximation, where investigating all subregions simultaneously is a computational costly …