Summary of On the Rashomon Ratio Of Infinite Hypothesis Sets, by Evzenie Coupkova et al.
On the Rashomon ratio of infinite hypothesis sets
by Evzenie Coupkova, Mireille Boutin
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR); 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 explores the concept of the Rashomon ratio in infinite families of classifiers, building upon previous work on finite families. The authors show that a large Rashomon ratio ensures that choosing the best-performing classifier among a random subset does not significantly increase the empirical loss. They provide examples and estimates of the Rashomon ratio for normally distributed classes with affine classifiers and modified Gram matrices using two-layer ReLU neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to measure the performance of different ways to classify things, like pictures or words. It shows that if you have a lot of different ways to do this, some of them will be really good at making mistakes, so you can pick one that is probably pretty good and not get worse results. They give examples of this idea working for different types of problems. |
Keywords
» Artificial intelligence » Relu
