Policy-Guided Embedding Search Learns Feature Transformations for Tabular Data

A new arXiv paper presents a method for learning feature transformations on tabular data by searching a continuous embedding space with a learned policy. The approach is hierarchical and invariant to the ordering of input features, allowing it to generalize across feature arrangements. Its stated aim is to build informative abstractions from raw features that improve downstream predictive performance.