Conditional stochastic optimization covers a variety of applications ranging from invariant
learning and causal inference to meta-learning. However, constructing unbiased gradient …

Abstract The Multilevel Monte Carlo (MLMC) approach usually works well when estimating
the expected value of a quantity which is a Lipschitz function of intermediate quantities, but if …

In this article, we present a review of the recent developments on the topic of Multilevel
Monte Carlo (MLMC) algorithms, in the paradigm of applications in financial engineering …

Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …

Many applications across sciences and technologies require a careful quantification of
nondeterministic effects to a system output, for example, when evaluating the system's …

The expected information gain is an important quality criterion of Bayesian experimental
designs, which measures how much the information entropy about uncertain quantity of …

Abstract Conditional Stochastic Optimization (CSO) covers a variety of applications ranging
from metalearning and causal inference to invariant learning. However, constructing …

In this paper, we develop a very efficient approach to the Monte Carlo estimation of the
expected value of partial perfect information (EVPPI) that measures the average benefit of …

An optimal experimental set‐up maximizes the value of data for statistical inferences. The
efficiency of strategies for finding optimal experimental set‐ups is particularly important for …

We present general principles for the design and analysis of unbiased Monte Carlo
estimators in a wide range of settings. Our estimators posses finite work-normalized …