Summary of Distribution-free Rates in Neyman-pearson Classification, by Mohammadreza M. Kalan et al.
Distribution-Free Rates in Neyman-Pearson Classification
by Mohammadreza M. Kalan, Samory Kpotufe
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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 investigates the problem of unbalanced classification in machine learning settings where one class is more prevalent than others. The goal is to minimize errors with respect to a specific distribution while keeping errors low for another distribution. A fixed set of classifiers is used to achieve this, and the paper provides a complete characterization of possible rates that can be achieved without knowing the underlying distributions. This is done by identifying a dichotomy between easy and hard classes based on a geometric condition related to VC dimension. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to do better with unbalanced data in machine learning. It tries to find the best way to classify things when one type of thing is much more common than others. The goal is to not make too many mistakes, but also not miss any important things. To solve this problem, the researchers use a set of pre-defined classifiers and show that there are limits on how well we can do without knowing more about the data. This is important for real-world applications where data might be imbalanced. |
Keywords
* Artificial intelligence * Classification * Machine learning
