arXiv AI October 9, 2026 By Kyaw Hpone Myint, Zhe Wu, Alexandre G. R. Day, Giri Iyengar

Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations

The paper presents a scalable method for meta‑learning interpretable decision trees by generating synthetic pre‑training data. It samples near‑optimal trees to create large, realistic datasets and uses the MetaTree transformer architecture to match the performance of real‑world pre‑training or costly optimal tree training. This approach cuts computational costs, offers flexible data generation, and supports efficient, scalable meta‑learning of decision trees.

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arXiv Machine Learning

Oct 9

MotherTree: Meta-learning on synthetic data improves decision tree training

MotherTree is a tabular transformer that meta‑learns to produce hard, axis‑aligned decision trees in a single forward pass. It is pre‑trained on a synthetic prior distribution using stochastic gradient descent, without needing reference trees for supervision. On controlled benchmarks, MotherTree matches or surpasses size‑matched trees from traditional algorithms and serves as a strong initializer for task‑specific fine‑tuning.

By Ziyuan Wang, Fredrik D. Johansson

llms efficiency benchmarks

arXiv Machine Learning

Jul 31

Search Strategies for Optimal Classification and Regression Trees

arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.

By Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirovi\'c

arXiv Machine Learning

Jul 1

Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again

arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.

By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin

benchmarks safety

arXiv AI

Jun 4

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning

arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.

By Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Provable Data Scaling Law for Meta Learning via Complexity Minimization

arXiv:2606. 02008v1 Announce Type: cross Abstract: Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-training data increases.

By Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui

arXiv Machine Learning

Jun 24

Experiments with Optimal Model Trees

arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.

By Sabino Francesco Roselli, Eibe Frank

benchmarks safety

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MotherTree: Meta-learning on synthetic data improves decision tree training

Search Strategies for Optimal Classification and Regression Trees

Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning

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