Summary of Partial Rankings Of Optimizers, by Julian Rodemann and Hannah Blocher
Partial Rankings of Optimizers
by Julian Rodemann, Hannah Blocher
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Machine learning practitioners seeking to evaluate optimizer performance now have a powerful new framework at their disposal. The recently introduced union-free generic depth function for partial orders/rankings is harnessed to fully exploit ordinal information, allowing for incomparability and avoiding aggregation pitfalls. This approach enables the identification of test functions that produce central or outlying rankings of optimizers, as well as assessments of benchmarking suite quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve created a way to compare different optimizer methods using many types of math problems (test functions). Our method uses a special kind of ranking system that takes into account how different the rankings are. This helps us find test functions that make some optimizers perform better or worse than others, and it lets us check if our benchmarking tests are fair. |
Keywords
* Artificial intelligence * Machine learning
