2 citations · 4 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
Practical Efficient Global Optimization is No-regret
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +3
Efficient global optimization (EGO) is one of the most widely used noise-free Bayesian optimization algorithms.It comprises the Gaussian process (GP) surrogate model and expected i…
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…
Convergence Rates of Constrained Expected Improvement
Haowei Wang, Jingyi Wang, Zhongxiang Dai +3
Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrain…
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…
On the convergence rate of noisy Bayesian Optimization with Expected Improvement
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open…