Summary of Lmems For Post-hoc Analysis Of Hpo Benchmarking, by Anton Geburek and Neeratyoy Mallik and Danny Stoll and Xavier Bouthillier and Frank Hutter
LMEMs for post-hoc analysis of HPO Benchmarking
by Anton Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed research applies Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of hyperparameter optimization (HPO) benchmarking runs. This approach allows for flexible and expressive modeling on entire experiment data, including information such as benchmark meta-features, providing deeper insights than current practices. The study demonstrates the effectiveness of LMEMs through a case study on the PriorBand paper’s experiment data, uncovering new findings not reported in the original work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make machine learning better by using a special kind of math called Linear Mixed-Effect Models. They’re going to use this math to look at lots of different ways that people optimize hyperparameters and see if one way is really better than the others. This will help us understand which methods work best in certain situations, which can be very useful for making new discoveries. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization
