Summary of On the Laplace Approximation As Model Selection Criterion For Gaussian Processes, by Andreas Besginow et al.
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
by Andreas Besginow, Jan David Hüwel, Thomas Pawellek, Christian Beecks, Markus Lange-Hegermann
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper proposes a novel approach to evaluating model performance, specifically for Gaussian process models, by introducing multiple metrics based on the Laplace approximation. The goal is to find a metric that balances accuracy, interpretability, and simplicity. The authors aim to overcome the limitations of previous metrics, such as likelihood, AIC, and dynamic nested sampling, which either lack performance or have significant runtime issues. The proposed approach allows for faster and high-quality model selection, making it more applicable in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian process models are a type of machine learning algorithm that’s used to make predictions based on incomplete data. In this paper, the researchers want to figure out how well these models do their job. They’re looking for a way to measure how good they are, and also how easy it is to understand why they made certain predictions. The current ways of measuring model performance have some problems – they either don’t work very well or take too long to calculate. So, the researchers came up with new methods that solve these issues. They tested their approach and found that it’s just as good as the best method currently available, but much faster. |
Keywords
* Artificial intelligence * Likelihood * Machine learning
