5 papers · 1 filter
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…
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…
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…
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…
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…