Learning to Simulate: Generative Metamodeling via Quantile Regression
Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn …
Knowledge Gradient for Selection with Covariates: Consistency and Computation
Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to consider in this paper the ranking and selection problem in the presence of …
Ranking and Selection with Covariates for Personalized Decision Making
We consider a ranking and selection problem in the context of personalized decision making, where the best alternative is not universal but varies as a function of observable …
Data-Driven Ranking and Selection: High-dimensional Covariates and General Dependence
This paper considers the problem of ranking and selection with covariates (R&S-C), which is first introduced by Shen et al. (2017) and aims to identify a decision rule that …
Ranking and Selection with Covariates
We consider a new ranking and selection problem in which the performance of each alternative depends on some observable random covariates. The best alternative is thus not constant …