Summary of Refereeing the Referees: Evaluating Two-sample Tests For Validating Generators in Precision Sciences, by Samuele Grossi et al.
Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences
by Samuele Grossi, Marco Letizia, Riccardo Torre
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); High Energy Physics – Phenomenology (hep-ph); Applications (stat.AP)
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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 robust methodology to evaluate the performance and computational efficiency of non-parametric two-sample tests, specifically designed for high-dimensional generative models in scientific applications such as particle physics. The study focuses on tests built from univariate integral probability measures, including the sliced Wasserstein distance, mean of the Kolmogorov-Smirnov statistics, and novel sliced Kolmogorov-Smirnov statistic. These metrics can be evaluated in parallel, allowing for fast and reliable estimates of their distribution under the null hypothesis. The paper also compares these metrics with other recently proposed unbiased Fréchet Gaussian Distance and Maximum Mean Discrepancy. The authors evaluate the proposed tests on various distributions, including correlated Gaussians, mixtures of Gaussians, and a particle physics dataset of gluon jets from the JetNet dataset. The results demonstrate that one-dimensional-based tests provide a level of sensitivity comparable to other multivariate metrics, but with significantly lower computational cost, making them ideal for evaluating generative models in high-dimensional settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to compare different models that generate lots of data. Scientists use these models to understand things like particle physics. The authors came up with a new way to test if one model is better than another. They used special math formulas and tested them on different kinds of data, including some from a big machine called the Large Hadron Collider. Their results show that their method is fast and reliable, and it can help scientists figure out which models are the best. |
Keywords
» Artificial intelligence » Probability
