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