38 citations · 87 across the 35 of their papers we have counts for
6 papers · 1 filter
Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Haoyuan Chen, Rui Tuo
Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burde…
Beyond State Space Representation: A General Theory for Kernel Packets
Liang Ding, Rui Tuo, Lu Zhou
Gaussian process (GP) regression provides a flexible, nonparametric framework for probabilistic modeling, yet remains computationally demanding in large-scale applications. For one…
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations
Haoyuan Chen, Liang Ding, Rui Tuo
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Matérn correlations whose smoothness parameter is a half-integer. The proposed a…
A Sparse Expansion For Deep Gaussian Processes
Liang Ding, Rui Tuo, Shahin Shahrampour
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models c…
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
Liang Ding, Rui Tuo, Xiaowei Zhang
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the…
Projection Pursuit Gaussian Process Regression
Gecheng Chen, Rui Tuo
A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer fro…