We study statistical inference and distributionally robust solution methods for stochastic
optimization problems, focusing on confidence intervals for optimal values and solutions that …

We investigate the use of distributionally robust optimization (DRO) as a tractable tool to
recover the asymptotic statistical guarantees provided by the central limit theorem, for …

We consider importance sampling to estimate the probability μ of a union of J rare events
H_j defined by a random variable x. The sampler we study has been used in spatial …

We study rare-event simulation for a class of problems where the target hitting sets of
interest are defined via modern machine learning tools such as neural networks and random …

In this article, we consider the problem of testing two separate families of hypotheses via a
generalization of the sequential probability ratio test. In particular, the generalized likelihood …

In this paper, we shall be concerned with geometric functionals and excursion probabilities
for some nonlinear transforms evaluated on Fourier components of spherical random fields …

We study the problem of designing good importance sampling (IS) schemes to simulate the
probability that a sophisticated predictor, built for instance from an off-the-shelf machine …

Importance sampling has been extensively studied as a variance reduction tool in rare-event
simulation. The design and efficiency of this method often relies on structural knowledge …

This paper develops asymptotic approximations of P (∫ T ef (t) dt> b) as b→∞ for a
homogeneous smooth Gaussian random field, f, living on a compact d-dimensional Jordan …

On the conditional distributions and the efficient simulations of exponential integrals of
Gaussian random fields Page 1 The Annals of Applied Probability 2014, Vol. 24, No. 4, 1691–1738 …