@inproceedings{koshil-etal-2025-context,
    title = "In-Context Learning of Soft Nearest Neighbor Classifiers for Intelligible Tabular Machine Learning",
    author = "Koshil, Mykhailo  and
      Feurer, Matthias  and
      Eggensperger, Katharina",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.15/",
    pages = "182--191",
    ISBN = "979-8-89176-268-8",
    abstract = "With in-context learning foundation models like TabPFN excelling on small supervised tabular learning tasks, it has been argued that ``boosted trees are not the best default choice when working with data in tables''. However, such foundation models are inherently black-box models that do not provide interpretable predictions. We introduce a novel learning task to train ICL models to act as a nearest neighbor algorithm, which enables intelligible inference and does not decrease performance empirically."
}