11 citations · 12 across the 4 of their papers we have counts for
4 papers
R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning
Duo Wang, Xinming Wang, Chao Wang +2
Multi-output Gaussian process (MGP) models have attracted significant attention for their flexibility and uncertainty-quantification capabilities, and have been widely adopted in m…
Local transfer learning Gaussian process modeling, with applications to surrogate modeling of expensive computer simulators
Xinming Wang, Simon Mak, John Miller +1
A critical bottleneck for scientific progress is the costly nature of computer simulations for complex systems. Surrogate models provide an appealing solution: such models are trai…
Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior
Wang Xinming, Li Yongxiang, Yue Xiaowei +1
Multi-output Gaussian process (MGP) is commonly used as a transfer learning method to leverage information among multiple outputs. A key advantage of MGP is providing uncertainty q…
Regularized Multi-output Gaussian Convolution Process with Domain Adaptation
Wang Xinming, Wang Chao, Song Xuan +2
Multi-output Gaussian process (MGP) has been attracting increasing attention as a transfer learning method to model multiple outputs. Despite its high flexibility and generality, M…