15 citations · 31 across the 18 of their papers we have counts for
8 papers · 1 filter
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
Wenjia Wang, Bing-Yi Jing
In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regress…
Reduced Rank Multivariate Kernel Ridge Regression
Wenjia Wang, Yi-Hui Zhou
In the multivariate regression, also referred to as multi-task learning in machine learning, the goal is to recover a vector-valued function based on noisy observations. The vector…
Uncertainty Quantification for Bayesian Optimization
Rui Tuo, Wenjia Wang
Bayesian optimization is a class of global optimization techniques. In Bayesian optimization, the underlying objective function is modeled as a realization of a Gaussian process. A…
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process
Wenjia Wang, Xiaowei Yue, Benjamin Haaland +1
In this paper, we investigate Gaussian process modeling with input location error, where the inputs are corrupted by noise. Here, the best linear unbiased predictor for two cases i…
On the Inference of Applying Gaussian Process Modeling to a Deterministic Function
Wenjia Wang
Gaussian process modeling is a standard tool for building emulators for computer experiments, which are usually used to study deterministic functions, for example, a solution to a…
Kriging prediction with isotropic Matérn correlations: Robustness and experimental design
Rui Tuo, Wenjia Wang
This work investigates the prediction performance of the kriging predictors. We derive some error bounds for the prediction error in terms of non-asymptotic probability under the u…
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
Wenjia Wang, Rui Tuo, C. F. Jeff Wu
Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simp…
Controlling Sources of Inaccuracy in Stochastic Kriging
Wenjia Wang, Benjamin Haaland
Scientists and engineers commonly use simulation models to study real systems for which actual experimentation is costly, difficult, or impossible. Many simulations are stochastic…