Summary of A Uniform Concentration Inequality For Kernel-based Two-sample Statistics, by Yijin Ni and Xiaoming Huo
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics
by Yijin Ni, Xiaoming Huo
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 paper presents a unifying framework for several widely used metrics in statistical and machine learning, which measure the discrepancy between two probability distributions. These metrics include Energy Distance, distance Covariance, Maximum Mean Discrepancy, and the Hilbert-Schmidt Independence Criterion. The authors demonstrate that these metrics can be unified under a general framework of kernel-based two-sample statistics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to optimize an objective function that measures the difference between two probability distributions. It looks at different ways to measure this difference, like Energy Distance and Maximum Mean Discrepancy. These methods are important in machine learning and statistics, and the authors find a way to put them together under one framework. |
Keywords
» Artificial intelligence » Machine learning » Objective function » Probability
