1 citations · 1 across the 5 of their papers we have counts for
5 papers
Preconditioned Truncated Single-Sample Estimators for Scalable Stochastic Optimization
Tianshi Xu, Difeng Cai, Hua Huang +2
Many large-scale stochastic optimization algorithms involve repeated solutions of linear systems or evaluations of log-determinants. In these regimes, computing exact solutions is…
HiGP: A high-performance Python package for Gaussian Process
Hua Huang, Tianshi Xu, Yuanzhe Xi +1
Gaussian Processes (GPs) are flexible, nonparametric Bayesian models widely used for regression and classification because of their ability to capture complex data patterns and qua…
H2-MG: A multigrid method for hierarchical rank structured matrices
Daria Sushnikova, George Turkiyyah, Edmond Chow +1
This paper presents a new fast iterative solver for large systems involving kernel matrices. Advantageous aspects of H2 matrix approximations and the multigrid method are hybridize…
Posterior Covariance Structures in Gaussian Processes
Difeng Cai, Edmond Chow, Yuanzhe Xi
In this paper, we present a comprehensive analysis of the posterior covariance field in Gaussian processes, with applications to the posterior covariance matrix. The analysis is ba…
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling
Shifan Zhao, Jiaying Lu, Ji Yang +2
Gaussian Process Regression (GPR) is widely used in statistics and machine learning for prediction tasks requiring uncertainty measures. Its efficacy depends on the appropriate spe…