Signals 4 · free daily AI digest

On Basis Function Selection for Sparse Gaussian Process Regression

Paper recorded by Signals 4 on 2026-09-22 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.LG · 机器学习 · first seen 2026-09-23

Abstract

Sparse Gaussian processes achieve $O(N)$ inference by replacing the kernel with an appropriate expansion in a fixed basis $\{φ_j\}$ on the input space. Given a compute budget $M \ll N$, practitioners conventionally truncate the basis to its first $M$ entries. Nothing in the formalism, however, prevents one from selecting only those $M$ basis functions that matter for the data at hand. This would a

Read on arXiv →

#12 most recent of 263 cs.LG papers we have recorded · ↑ newer: Label-Efficient Learning for Ground-Based Sky-Image Classification: A · ↓ older: MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer'
Cite this page: On Basis Function Selection for Sparse Gaussian Process Regression: the #12 most recent of 263 cs.LG papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/on-basis-function-selection-for-sparse-gaussian-process-regression.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions