Summary of Credal Two-sample Tests Of Epistemic Uncertainty, by Siu Lun Chau et al.
Credal Two-Sample Tests of Epistemic Uncertainty
by Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A new hypothesis testing framework is introduced, called credal two-sample testing, which compares convex sets of probability measures representing aleatoric and epistemic uncertainties. This approach generalizes traditional two-sample tests by enabling the integration of epistemic uncertainty, allowing for reasoning about equality, inclusion, intersection, and mutual exclusivity. The framework focuses on finitely generated credal sets derived from i.i.d. samples from multiple distributions and introduces a permutation-based solution for this class of problems. This approach leads to more robust and credible conclusions, with kernel-based implementations for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test hypotheses is introduced! Imagine you have two groups of things that might be different in some way, but you’re not sure exactly how they differ. This framework helps you figure out if the differences between the two groups are significant or just random. It does this by looking at sets of possible outcomes for each group, which can include things like “there might be a little bit of X” and “there might be a lot of Y”. By comparing these sets, the framework can tell you if the differences between the two groups are real or just a fluke. |
Keywords
» Artificial intelligence » Probability
