# Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization

arXiv: https://arxiv.org/abs/2403.04720 · HTML: https://mlsanity.com/paper/2403.04720/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [Dataset2Vec: Learning Dataset Meta-Features](https://mlsanity.com/paper/1905.11063/) (2019)
- [Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces](https://mlsanity.com/paper/2311.10051/) (2023)

## Abstract (arXiv)

Effectively representing heterogeneous tabular datasets for meta-learning purposes remains an open problem. Previous approaches rely on predefined meta-features, for example, statistical measures or landmarkers. The emergence of dataset encoders opens new possibilities for the extraction of meta-features because they do not involve any handmade design. Moreover, they are proven to generate dataset representations with desired spatial properties. In this research, we evaluate an encoder-based approach to one of the most established meta-tasks - warm-starting of the Bayesian Hyperparameter Optimization. To broaden our analysis we introduce a new approach for representation learning on tabular data based on [Tomoharu Iwata and Atsutoshi Kumagai. Meta-learning from Tasks with Heterogeneous Attribute Spaces. In Advances in Neural Information Processing Systems, 2020]. The validation on over 100 datasets from UCI and an independent metaMIMIC set of datasets highlights the nuanced challenges in representation learning. We show that general representations may not suffice for some meta-tasks where requirements are not explicitly considered during extraction.