arXiv:2607. 01548v1 Announce Type: cross Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization.
By Ege Onur Taga, Yilin Zhuang, M. Emrullah Ildiz, Petros Mol, Abhimanyu Das, Karthik Duraisamy, Samet Oymak
Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data.
arXiv:2606. 07704v1 Announce Type: cross Abstract: Symbolic regression aims to uncover explicit scientific laws from data.
By Zeyu Xia, Jun Zhu, Dong Yan
arXiv:2606. 08800v1 Announce Type: new Abstract: In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains.
By Varun Khurana, Vijval Ekbote, Vashu Chauhan, Yaman Kumar Singla, Rajiv Ratn Shah, Balaji Krishnamurthy
arXiv:2607. 16255v1 Announce Type: cross Abstract: A crucial step in machine learning pipelines is to present each entity with features or attributes that are representative of the characteristics of the processed entities.
By Aria Nourbakhsh, Beno\^it Alcaraz, Christoph Schommer
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
By Muhammad Rajabinasab, Arthur Zimek