Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911251100073984 |
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| author | Myint, Kyaw Hpone Wu, Zhe Day, Alexandre G. R. Iyengar, Giri |
| author_facet | Myint, Kyaw Hpone Wu, Zhe Day, Alexandre G. R. Iyengar, Giri |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations Myint, Kyaw Hpone Wu, Zhe Day, Alexandre G. R. Iyengar, Giri Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.04000 |