# Constructing Gaussian Processes via Samplets

arXiv: https://arxiv.org/abs/2411.07277 · HTML: https://mlsanity.com/paper/2411.07277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2024.

## Abstract (arXiv)

Gaussian Processes face two primary challenges: constructing models for large datasets and selecting the optimal model. This master's thesis tackles these challenges in the low-dimensional case. We examine recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. Utilizing this model, we propose a Samplet-based approach to efficiently construct and train the Gaussian Processes, reducing the cubic computational complexity to a log-linear scale. This method facilitates optimal regression while maintaining efficient performance.