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.

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arXiv AI

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TabJoinBench: A Benchmark for Joinable Table Discovery

arXiv:2610.00817v1 Announce Type: cross Abstract: Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstrea...

By Sandipan De, Jin Wang, Vivek Gupta

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arXiv Machine Learning

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RelICL: Training-free Relational Learning with Tabular Foundation Models

RelICL: Training-free Relational Learning with Tabular Foundation Models proposes a new method for relational learning that addresses two key issues of deep feature synthesis—feature explosion and interaction blindness—by propagating and fusing information step by step through the schema graph using a tabular foundation model. The approach retains the benefits of DFS while improving scalability and performance. Experiments on RelBench tasks show that RelICL performs on par with the strongest DFS-based approach.

By Simon Forbat, Rainer Gemulla

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arXiv Machine Learning

Jun 2

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks

arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.

By Andrej Tschalzev, Nick Erickson, Yuyang Wang, Huzefa Rangwala, Stefan L\"udtke, Heiner Stuckenschmidt, Christian Bartelt

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arXiv AI

Jun 30

SemJoin: Semantic Join Optimization

arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.

By Christopher Gou, Aditya Banerjee, Jiaxuan Wang, Chunwei Liu

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arXiv AI

Jun 10

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.

By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu

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arXiv AI

Jul 28

TopoFE: topology-aware LLM-guided Automated Feature Engineering

arXiv:2607. 23286v1 Announce Type: new Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space.

By Sha Li, Naren Ramakrishnan

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TabJoinBench: A Benchmark for Joinable Table Discovery

RelICL: Training-free Relational Learning with Tabular Foundation Models

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SemJoin: Semantic Join Optimization

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TopoFE: topology-aware LLM-guided Automated Feature Engineering