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

arXiv:2403.04720v1 Announce Type: new
Abstract: Effectively representing heterogeneous tabular datasets for meta-learning remains an open problem. Previous approaches rely on predefined meta-features, for example, statistical measures or landmarkers. Encoder-based models, such as Dataset2Vec, allow us to extract significant meta-features automatically without human intervention. This research introduces a novel encoder-based representation of tabular datasets implemented within the liltab package available on GitHub https://github.com/azoz01/liltab. Our package is based on an established model for heterogeneous tabular data proposed in [Iwata and Kumagai, 2020]. …

advances arxiv cs.lg encoder hyperparameter information meta meta-learning optimization processing spaces systems tasks type warm