5 papers
Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
Haoyuan Chen, Emil Constantinescu, Vishwas Rao +1
The Madden--Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predicti…
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Wenyuan Zhao, Haoyuan Chen, Tie Liu +2
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the co…
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…
Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner
Haoyuan Chen, Rui Tuo
Gaussian processes (GPs) are widely used in non-parametric Bayesian modeling, and play an important role in various statistical and machine learning applications. In a variety task…
Representing Additive Gaussian Processes by Sparse Matrices
Lu Zou, Haoyuan Chen, Liang Ding
Among generalized additive models, additive Matérn Gaussian Processes (GPs) are one of the most popular for scalable high-dimensional problems. Thanks to their additive structure a…