Summary of Explainable Learning with Gaussian Processes, by Kurt Butler et al.
Explainable Learning with Gaussian Processes
by Kurt Butler, Guanchao Feng, Petar M. Djuric
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles the problem of explainable artificial intelligence (XAI) in Gaussian process regression (GPR), focusing on feature attribution methods. It extends existing literature by providing a principled approach to defining attributions under model uncertainty, resulting in interpretable and closed-form expressions for feature attributions. The authors demonstrate the versatility and robustness of this approach through theoretical and experimental analyses, showing that it is more accurate and computationally efficient than current approximations. They also provide open-source code for the project, freely available under the MIT license. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary XAI aims to make AI more understandable by explaining how machines learn. The paper focuses on a specific type of machine learning called Gaussian process regression (GPR). It helps us understand which parts of the data GPR is using to make predictions. The researchers developed new ways to explain these predictions and showed that their methods are accurate, efficient, and easy to use. |
Keywords
* Artificial intelligence * Machine learning * Regression
