{"ID":2845151,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04000","arxiv_id":"2511.04000","title":"Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations","abstract":"Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an efficient, scalable method for generating synthetic pre-training data to enable meta-learning of decision trees. Our approach samples near-optimal decision trees synthetically, creating large-scale, realistic datasets. Using the MetaTree transformer architecture, we demonstrate that this method achieves performance comparable to pre-training on real-world data or with computationally expensive optimal decision trees. This strategy significantly reduces computational costs, enhances data generation flexibility, and paves the way for scalable and efficient meta-learning of interpretable decision tree models.","short_abstract":"Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an efficient, scalable method for generating synthetic pre-training data to enable meta-learning of decision trees. Our approach samples near-optimal decision trees synthetically, creatin...","url_abs":"https://arxiv.org/abs/2511.04000","url_pdf":"https://arxiv.org/pdf/2511.04000v1","authors":"[\"Kyaw Hpone Myint\",\"Zhe Wu\",\"Alexandre G. R. Day\",\"Giri Iyengar\"]","published":"2025-11-06T02:50:23Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CL\",\"stat.ML\"]","methods":"[\"Transformer\"]","has_code":false}