Summary of Online Performance Estimation with Unlabeled Data: a Bayesian Application Of the Hui-walter Paradigm, by Kevin Slote et al.
Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm
by Kevin Slote, Elaine Lee
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 proposed method adapts the Hui-Walter paradigm from epidemiology and medicine to machine learning, enabling the estimation of key performance metrics without labeled data. This approach partitions data into latent classes to simulate multiple populations and independently trains models to replicate tests, allowing for accurate model assessment in dynamic and uncertain data conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning practitioners often work with accessible, static, and labeled data, but this assumption doesn’t always hold true. The reality is that data can be private, encrypted, difficult-to-measure, or unlabeled. This paper proposes a method to bridge the gap between these assumptions and reality. It’s based on the Hui-Walter paradigm from epidemiology and medicine, which allows for estimating performance metrics without labeled data. |
Keywords
* Artificial intelligence * Machine learning
