Summary of Contraction Rates For Conjugate Gradient and Lanczos Approximate Posteriors in Gaussian Process Regression, by Bernhard Stankewitz and Botond Szabo
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by Bernhard Stankewitz, Botond Szabo
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 This paper explores Gaussian process (GP) regression models, a staple in modern statistics and machine learning due to their flexibility and theoretical tractability. The true posterior is explicitly given, but numerical evaluations rely on the inversion of the augmented kernel matrix, which requires up to O(n^3) operations. For large sample sizes, this becomes computationally infeasible, necessitating approximate methods with limited theoretical underpinning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian process regression models are super important for statistics and machine learning because they’re flexible and easy to work with mathematically. The problem is that when you want to use them with really big datasets, it takes a long time to do the calculations. To make things faster, people usually use approximations, but these aren’t based on strong math. |
Keywords
» Artificial intelligence » Machine learning » Regression
