Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations
with respect to some target distribution which adapt themselves to obtain better estimators …

We develop and analyze a dynamic finite-budget allocation scheme for stratified sampling
for general purposes on the unit hypercube with adaptive variance reduction applied by …

We establish radial importance sampling under directional stratification and construct its
easy-to-implement algorithm for estimating the probability of failure in structural reliability …

Simplices are the fundamental domain when integrating over convex polytopes. The aim of
this work is to establish a novel framework of Monte Carlo integration over simplices …

To minimize the variance of Monte Carlo estimators, we develop a novel exponential
embedding technique that extends the classical concept of sufficient statistics in importance …

Adaptive Monte Carlo variance reduction is an effective framework for running a Monte
Carlo simulation along with a parameter search algorithm for variance reduction, whereas …

We analyze the optimized adaptive importance sampler (OAIS) for performing Monte Carlo
integration with general proposals. We leverage a classical result which shows that the bias …

We construct and investigate an adaptive variance reduction framework in which both
importance sampling and control variates are employed. The three lines (Monte Carlo …

Stochastic programming models can lead to very large-scale optimization problems for
which it may be impossible to enumerate all possible scenarios. In such cases, one adopts a …

We introduce a new class of adaptive importance samplers leveraging adaptive optimisation
tools, which we term AdaOAIS. We build on Optimised Adaptive Importance Samplers …