A linear-combination conditioning strategy for GPs achieves exponential convergence to machine precision with r≈100 combinations at O(Tr²) cost for smooth kernels and simple domains.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
The paper proves identifiability of drifting fields for companion-elliptic kernels and shows that field convergence plus a C0 observable recovers weak convergence even without tightness.
citing papers explorer
-
Fast and accurate conditioning for large-scale and online Gaussian process prediction problems
A linear-combination conditioning strategy for GPs achieves exponential convergence to machine precision with r≈100 combinations at O(Tr²) cost for smooth kernels and simple domains.
-
Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families
The paper proves identifiability of drifting fields for companion-elliptic kernels and shows that field convergence plus a C0 observable recovers weak convergence even without tightness.