# LMEMs for post-hoc analysis of HPO Benchmarking

arXiv: https://arxiv.org/abs/2408.02533 · HTML: https://mlsanity.com/paper/2408.02533/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO](https://mlsanity.com/paper/2109.06716/) (2021)
- [HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML](https://mlsanity.com/paper/2106.06257/) (2021)
- [Pre-trained Gaussian Processes for Bayesian Optimization](https://mlsanity.com/paper/2109.08215/) (2021)
- [YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization](https://mlsanity.com/paper/2109.03670/) (2021)
- [Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets](https://mlsanity.com/paper/1709.09500/) (2017)
- [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2306.12370/) (2023)

## Built on by (most cited first, 2 of 2)

- [Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation](https://mlsanity.com/paper/2606.08679/) (2026)
- [Multi-objective Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2511.08371/) (2025)

## Abstract (arXiv)

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.