The paper introduces TOPOFE, a framework that treats automatic feature engineering for tabular data as a graph-structured multi-island evolutionary search. Each island explores a semantically coherent family of transformations using LLM-guided mutation and crossover, while a Prompt Adaptation Memory steers proposals based on accept/reject feedback. TOPOFE dynamically learns a directed topology graph to coordinate cross-island transfer, enabling the discovery of compositional feature programs that outperform state‑of‑the‑art methods on 29 datasets and produce lower redundancy and higher representational coverage.
By Sha Li, Naren Ramakrishnan
arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.
By Bingheng Li, Junyang Cai, Yupeng Zhang, Bistra Dilkina, Jayant Kalagnanam, Dzung T. Phan
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
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
By Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
arXiv:2606. 09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE).
By Ankai Hao, Ke Chen, Huan Li, Lidan Shou
The paper presents an iterative framework that uses large language models (LLMs) to automatically extract interpretable, schema‑bound categorical features from unstructured text for use in tabular prediction models. A generator LLM proposes semantic definitions, an extractor LLM materializes the features, and a downstream tabular model evaluates their predictive performance, with error‑driven natural‑language feedback guiding the search. Across three public datasets, the error‑driven loop speeds up feature discovery up to three times and the resulting features outperform any subset when combined with TF‑IDF and dense embeddings, while also providing instance‑level interpretability through SHAP importance rankings and a semantic audit trail.
By Merwan Barlier, Blaz Skrlj