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.

翻译：有效表示异构表格数据集以进行元学习仍是一个未解决的问题。以往的方法依赖于预定义的元特征，例如统计度量或标记特征。基于编码器的模型（如Dataset2Vec）使我们能够在无需人工干预的情况下自动提取重要的元特征。本研究提出了一种新颖的基于编码器的表格数据集表示方法，并在liltab包中实现，该包可在GitHub上获取：https://github.com/azoz01/liltab。我们的包基于[Tomoharu Iwata和Atsutoshi Kumagai在《从具有异构属性空间的任务中进行元学习》中提出的异构表格数据模型，该文发表于《神经信息处理系统进展》，2020年]。所提出的方法采用不同的模型对特征关系进行编码，与Dataset2Vec等现有方法相比生成了替代性表示。这两种方法均利用了数据集相似性学习的基本假设。在本工作中，我们在两个常见的元任务——表示整个数据集和超参数优化热启动——上评估了Dataset2Vec和liltab。然而，在独立元MIMIC数据集上的验证凸显了表示学习中的细微挑战。我们表明，对于某些在提取过程中未明确考虑要求的元任务，通用表示可能不足以胜任。