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

We survey the active interface of statistical learning methods and quantitative finance
models. Our focus is on the use of statistical surrogates, also known as functional …

This paper develops a new neural network architecture for modeling spatial distributions (ie
distributions on R d) which is more computationally efficient than a traditional fully …

We investigate two new strategies for the numerical solution of optimal stopping problems in
the regression Monte Carlo (RMC) framework proposed by Longstaff and Schwartz in 2001 …

Modeling counterparty risk is computationally challenging because it requires the
simultaneous evaluation of all the trades with each counterparty under both market and …

Electrochemical CO 2 capture technologies have been found to consume less energy than
the industry standard of thermal separations, but their real-world applicability requires that …

Game theory finds nowadays a broad range of applications in engineering and machine
learning. However, in a derivative-free, expensive black-box context, very few algorithmic …

We systematically describe the problem of simultaneous surrogate modeling of mixed
variables (ie, continuous, integer and categorical variables) in the Bayesian optimization …

We consider the problem of learning the level set for which a noisy black-box function
exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian …

Bayesian optimal design of experiments (BODEs) have been successful in acquiring
information about a quantity of interest (QoI) which depends on a black-box function. BODE …