@inproceedings{koo-zhang-2025-automated,
    title = "Automated search algorithm for optimal generalized linear mixed models ({GLMM}s)",
    author = "Koo, Miryeong  and
      Zhang, Jinming",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-main.38/",
    pages = "352--358",
    ISBN = "979-8-218-84228-4",
    abstract = "Only a limited number of predictors can be included in a generalized linear mixed model (GLMM) due to estimation algorithm divergence. This study aims to propose a machine learning based algorithm (e.g., random forest) that can consider all predictors without the convergence issue and automatically searches for the optimal GLMMs."
}