activity
20182021
most citedSurrogate-Based Simulation Optimization

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

stat.ML2021

High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids

Liang Ding, Rui Tuo, Xiaowei Zhang

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…

math.OC20212 cited

Surrogate-Based Simulation Optimization

L. Jeff Hong, Xiaowei Zhang

Simulation models are widely used in practice to facilitate decision-making in a complex, dynamic and stochastic environment. But they are computationally expensive to execute and…

stat.ME2019

Distributionally Robust Selection of the Best

Weiwei Fan, L. Jeff Hong, Xiaowei Zhang

Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data re…

q-fin.MF2018

Affine Jump-Diffusions: Stochastic Stability and Limit Theorems

Xiaowei Zhang, Peter W. Glynn

Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. Me…

stat.ME2018

Scalable Stochastic Kriging with Markovian Covariances

Liang Ding, Xiaowei Zhang

Stochastic kriging is a popular technique for simulation metamodeling due to its exibility and analytical tractability. Its computational bottleneck is the inversion of a covarianc…

stat.ME2018

Stochastic Kriging for Inadequate Simulation Models

Lu Zou, Xiaowei Zhang

Stochastic kriging is a popular metamodeling technique for representing the unknown response surface of a simulation model. However, the simulation model may be inadequate in the s…