Summary of Constructing Gaussian Processes Via Samplets, by Marcel Neugebauer
Constructing Gaussian Processes via Samplets
by Marcel Neugebauer
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent master’s thesis addresses two primary challenges in Gaussian Process (GP) modeling: constructing models for large datasets and selecting the optimal model. In the low-dimensional case, the study focuses on optimizing GP construction by leveraging recent convergence results to identify models with optimal convergence rates and pinpoint essential parameters. A Samplet-based approach is proposed to efficiently construct and train GPs, reducing computational complexity from cubic to log-linear scales while maintaining optimal regression performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers learn from small datasets of information is the focus of this research paper. The problem is that current methods for building these learning models (called Gaussian Processes) take a long time to work with large amounts of data and it’s hard to know which model will work best. This study figures out how to make these models better by using recent discoveries about how they converge, or get closer to the right answer. The result is a new way to build these models that is much faster and works just as well. |
Keywords
* Artificial intelligence * Regression
