4 citations · 7 across the 12 of their papers we have counts for
14 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…
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Yilin Zheng, Haowei Wang, Szu Hui Ng +1
Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisitio…
Local Constrained Bayesian Optimization
Jing Jingzhe, Fan Zheyi, Szu Hui Ng +1
Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality. We propose Local Constrained Bayesian Optim…
Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Jingyi Wang, Haowei Wang, Szu Hui Ng +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative r…
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…
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…
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization
Zheyi Fan, Wenyu Wang, Szu Hui Ng +1
Local Bayesian optimization is a promising practical approach to solve the high dimensional black-box function optimization problem. Among them is the approximated gradient class o…
A Novel Framework for Improving the Breakdown Point of Robust Regression Algorithms
Zheyi Fan, Szu Hui Ng, Qingpei Hu
We present an effective framework for improving the breakdown point of robust regression algorithms. Robust regression has attracted widespread attention due to the ubiquity of out…
A model aggregation approach for high-dimensional large-scale optimization
Haowei Wang, Ercong Zhang, Szu Hui Ng +1
Bayesian optimization (BO) has been widely used in machine learning and simulation optimization. With the increase in computational resources and storage capacities in these fields…
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
Shouri Hu, Haowei Wang, Zhongxiang Dai +2
The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical performances in many applications…
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…