1 citations · 1 across the 2 of their papers we have counts for
3 papers
math.OC2022
A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization
Kai Jia, Xiaojun Duan, Zhengming Wang +1
This paper presents a method called sampling-computation-optimization (SCO) to design batch Bayesian optimization. SCO does not construct new high-dimensional acquisition functions…
math.ST2019
Optimal Sliced Latin Hypercube Designs with Slices of Arbitrary Run Sizes
Jing Zhang, Jin Xu, Kai Jia +2
Sliced Latin hypercube designs (SLHDs) are widely used in computer experiments with both quantitative and qualitative factors and in batches. Optimal SLHDs achieve better space-fil…
math.ST2019★ 1 cited
Sliced Latin hypercube designs with arbitrary run sizes
Jin Xu, Xu He, Xiaojun Duan +1
Latin hypercube designs achieve optimal univariate stratifications and are useful for computer experiments. Sliced Latin hypercube designs are Latin hypercube designs that can be p…