Abstract Machine learning models are widely applied in various fields. Stakeholders often
use post-hoc feature importance methods to better understand the input features' …

When working with models that allow for many candidate solutions, simulation practitioners
can benefit from screening out unacceptable solutions in a statistically controlled way …

In this article, we study the problem of selecting a subset with the best of a finite number of
alternatives under a fixed simulation budget. Our work aims to maximize the posterior …

This study focuses on the subset selection problem of computational statistics and deploys
the rank-biserial correlation (RBSC) based deck generation algorithm (RBSC-SubGen)[ 1] in …

Ranking and selection (R&S) is the problem of identifying the optimal alternative from
multiple alternatives through sampling them. In the existing R&S literature, sampling …

Screening procedures for ranking and selection have received less attention than selection
procedures, yet they serve as a cheap and powerful tool for decision making under …

Plausible inference is a growing body of literature that treats stochastic simulation as a gray
box when structural properties of the simulation output performance measures as a function …

This study focuses on a specific type of subset selection problem, which is constrained in
terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such …

This study focuses on the algorithm RBSC-SubGen, which is originally offered for deck
generation. We first study the resilience of RBSC-SubGen against various hyper-parameters …

This paper studies methods that identify plausibly near-optimal solutions based on
simulation results obtained from only a small subset of feasible solutions. We do so by …