LMEMs for post-hoc analysis of HPO Benchmarking

Anton Merlin Geburek, Neeratyoy Mallik, Danny Stoll, Xavier Bouthillier, Frank Hutter

Keywords: Hyperparameter Optimization, Benchmarking, Significance testing

TL;DR: Applying model-based significance testing for flexible post-hoc analysis of HPO Benchmarking data.

Abstract:

The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algorithms and benchmarks steadily added by
the community.
However, current benchmarking practices using averaged performance across many datasets may obscure key differences between HPO methods, especially for pairwise comparisons.
In this work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of HPO benchmarking runs.
LMEMs allow flexible and expressive modeling on the entire experiment data, including information such as benchmark meta-features, offering deeper insights than current analysis practices.
We demonstrate this through a case study on the PriorBand paper's experiment data to find insights not reported in the original work.

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Submission Number: 21