  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 [Tomoharu Iwata
and Atsutoshi Kumagai. Meta-learning from Tasks with Heterogeneous Attribute
Spaces. In Advances in Neural Information Processing Systems, 2020]. The
proposed approach employs a different model for encoding feature relationships,
generating alternative representations compared to existing methods like
Dataset2Vec. Both of them leverage the fundamental assumption of dataset
similarity learning. In this work, we evaluate Dataset2Vec and liltab on two
common meta-tasks -- representing entire datasets and hyperparameter
optimization warm-start. However, validation on an independent metaMIMIC
dataset 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.
