Summary of Compactly-supported Nonstationary Kernels For Computing Exact Gaussian Processes on Big Data, by Mark D. Risser et al.
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
by Mark D. Risser, Marcus M. Noack, Hengrui Luo, Ronald Pandolfi
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME)
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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 The proposed work introduces an alternative kernel for Gaussian processes (GPs) that can accommodate large datasets and non-stationarity. This novel kernel is embedded within a fully Bayesian GP model, enabling the analysis of massive datasets. The authors demonstrate the superior performance of their approach compared to existing exact and approximate GP methods on various synthetic datasets. Additionally, they apply their method to predict daily maximum temperature based on over one million measurements, outperforming state-of-the-art methods in the Earth sciences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to use Gaussian processes (GPs) that can handle big datasets and changing patterns. The approach combines a special kernel with a Bayesian GP model, making it possible to analyze massive amounts of data. The authors test their method on fake data and show that it performs better than other GP methods. They also apply this approach to predict daily temperature based on over one million measurements, achieving better results than current methods in the field. |
Keywords
* Artificial intelligence * Temperature
