arXiv Machine Learning July 21, 2026 By Serafeim Papadias, Kostas Patroumpas, Dimitrios Skoutas

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

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arXiv:2602. 02025v2 Announce Type: replace-cross Abstract: ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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