Summary of Fine-grained Uncertainty Quantification Via Collisions, by Jesse Friedbaum et al.
Fine-Grained Uncertainty Quantification via Collisions
by Jesse Friedbaum, Sudarshan Adiga, Ravi Tandon
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Statistics Theory (math.ST); 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 This paper proposes a new metric for quantifying aleatoric uncertainty in classification problems, which is based on the rate of class collisions. The prevalence of class collisions is defined as the same input being observed in different classes, and it is used to define the collision matrix, a novel measure of uncertainty. The authors discuss several applications of this metric, establish its mathematical properties, and show its relationship with existing uncertainty quantification methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure how sure we are about what something is when we’re trying to put it into one category or another. They call this “aleatoric uncertainty.” What they do is count how many times the same thing can be in different categories, and that helps them understand how hard it is to tell things apart. This idea has some useful applications, like helping us figure out which things are most likely to be in a certain group. It also connects with other ways we think about uncertainty, like something called the Bayes error rate. |
Keywords
* Artificial intelligence * Classification
