Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

Motivated by the poor performance of cross-validation in settings where data are scarce, we
propose a novel estimator of the out-of-sample performance of a policy in data-driven …

We study a constructive procedure that approximates Gateaux derivatives for statistical
functionals by finite-differencing, with attention to causal inference functionals. We focus on …

The use of mathematical models has become increasingly prevalent in pharmacological
fields, particularly in drug development processes. These models are instrumental in tasks …

With the rapid development of wireless networks and smart devices, spatial crowdsourcing
(SC) has become increasingly prevalent. The key issue in SC is efficiently assigning spatial …

In this note, we identify a statistically significant error in naively estimating the expected profit
in a data-driven newsvendor model, and we show how to correct the error. In particular, we …

Optimization software enables the solution of problems with millions of variables and
associated parameters. These parameters are, however, often uncertain and represented …

We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient
(PG) losses, for the predict-then-optimize framework. These losses directly approximate the …

The intersection of causal inference and machine learning for decision-making is rapidly
expanding, but the default decision criterion remains an average of individual causal …

In data-driven optimization, the sample performance of the obtained decision typically incurs
an optimistic bias against the true performance, a phenomenon commonly known as the …