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

arXiv: https://arxiv.org/abs/2602.02025 · HTML: https://mlsanity.com/paper/2602.02025/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 15 of 15)

- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [SANTOS: Relationship-based Semantic Table Union Search](https://mlsanity.com/paper/2209.13589/) (2022)
- [Exploring Large Language Models for Feature Selection: A Data-centric Perspective](https://mlsanity.com/paper/2408.12025/) (2024)
- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization](https://mlsanity.com/paper/2502.10648/) (2025)
- [RACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graph](https://mlsanity.com/paper/2409.14556/) (2024)
- [Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?](https://mlsanity.com/paper/1704.00485/) (2017)
- [FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables](https://mlsanity.com/paper/2403.06367/) (2024)
- [Instance-Optimal Acyclic Join Processing Without Regret: Engineering the Yannakakis Algorithm in Column Stores](https://mlsanity.com/paper/2411.04042/) (2024)
- [LLM4FS: Leveraging Large Language Models for Feature Selection](https://mlsanity.com/paper/2503.24157/) (2025)
- [Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees](https://mlsanity.com/paper/2504.03279/) (2025)

## Abstract (arXiv)

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. Feature augmentation addresses this problem by automatically discovering and joining additional tables to enrich a base table with predictive features. However, scaling feature augmentation to complex schemas with many tables and multi-hop relationships is challenging. It requires exploring a large space of join paths, executing costly joins, and selecting useful features from noisy results. Existing approaches suffer from either limited effectiveness or efficiency. Restricting exploration to simple joins limits predictive performance, while more expressive methods rely on expensive training data, lack scalability, or fail to fully exploit schema-level semantics. We present Hippasus, a cost-aware, LLM-augmented feature discovery framework over relational schemas that addresses these challenges. Hippasus combines lightweight statistical signals with adaptive semantic reasoning, invoking stronger (LLM-based) analysis only when necessary. It further introduces efficient multi-way join execution with cross-path feature consolidation, and a hybrid feature selection strategy that integrates statistical relevance with semantic refinement. Experiments on real-world datasets show that Hippasus improves feature augmentation accuracy by up to 26.8% over state-of-the-art methods, while achieving a favorable effectiveness-cost tradeoff.