Summary of Bayesian Meta Learning For Trustworthy Uncertainty Quantification, by Zhenyuan Yuan et al.
Bayesian meta learning for trustworthy uncertainty quantification
by Zhenyuan Yuan, Thinh T. Doan
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 Trust-Bayes framework optimizes Bayesian meta learning by considering trustworthy uncertainty quantification. This framework, which doesn’t rely on explicit assumptions about prior models or distributions, defines trustworthy uncertainty as intervals dependent on predictive distributions with a specified probability. The paper provides lower bounds for the probabilities of capturing ground truth and analyzes sample complexity. A case study using Gaussian process regression is conducted to verify and compare Trust-Bayes with the Meta-prior algorithm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We explore a way to make predictions more reliable by checking if our uncertainty estimates are trustworthy. This means we want to ensure that our intervals (prediction ranges) capture the true values most of the time. We introduce a new method, Trust-Bayes, which does this without making assumptions about what kind of models or distributions might be used beforehand. The paper shows how well this approach works and compares it to another popular method. |
Keywords
» Artificial intelligence » Meta learning » Probability » Regression
