2 papers
cs.LG2025
Scaling Gaussian Process Regression with Full Derivative Observations
Daniel Huang
We present a scalable Gaussian Process (GP) method called DSoftKI that can fit and predict full derivative observations. It extends SoftKI, a method that approximates a kernel via…
stat.ML2024
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation
Chris Camaño, Daniel Huang
We introduce Soft Kernel Interpolation (SoftKI), a method that combines aspects of Structured Kernel Interpolation (SKI) and variational inducing point methods, to achieve scalable…