High-Dimensional

Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions

Nested simulation concerns estimating functionals of a conditional expectation via simulation. In this paper, we propose a new method based on kernel ridge regression to exploit the smoothness of the conditional expectation as a function of the …

Sample and Computationally Efficient Stochastic Kriging in High Dimensions

Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because, in general, the sample …

High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids

High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the curse of dimensionality. The algorithm consists of two stages. First, …

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 stipulates the best alternative as a function of the observable covariates. We …