4 citations · 5 across the 6 of their papers we have counts for
8 papers
Principled Design of Expected Improvement for Continuous Simulation Optimization
Songhao Wang, Jianglin Xia, Haowei Wang +1
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design cho…
An Online Non-Stationary Simulation Optimization Approach Based on Regime Switching
Jianglin Xia, Haowei Wang, Songhao Wang +1
Dynamic and evolving operational and economic environments present significant challenges for decision-making. We explore a simulation optimization problem characterized by non-sta…
Axiomatic Equilibrium Selection: The Case of Generic Extensive Form Games
Srihari Govindan, Robert B. Wilson
A solution concept that is a refinement of Nash equilibria selects for each finite game a nonempty collection of closed and connected subsets of Nash equilibria as solutions. We im…
Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Models
Mingyu Pu, Songhao Wang, Haowei Wang +1
Gaussian Process (GP) models are widely utilized as surrogate models in scientific and engineering fields. However, standard GP models are limited to continuous variables due to th…
Enhanced Global Optimization with Parallel Global and Local Structures
Haowei Wang, Songhao Wang, Qun Meng +1
In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To…
Combined Global and Local Search for Optimization with Gaussian Process Models
Qun Meng, Songhao Wang, Szu Hui Ng
Gaussian process (GP) model based optimization is widely applied in simulation and machine learning. In general, it first estimates a GP model based on a few observations from the…
A Data-Driven Bayesian Nonparametric Approach for Black-Box Optimization
Haowei Wang, Xun Zhang, Szu Hui Ng +1
We present a data-driven Bayesian nonparametric approach for global optimization (DaBNO) of stochastic black-box function. The function value depends on the distribution of a rando…
A Multi-Level Simulation Optimization Approach for Quantile Functions
Songhao Wang, Szu Hui Ng, William Benjamin Haskell
Quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles o…