AI RESEARCH
Minimax Rates and Spectral Distillation for Tree Ensembles
arXiv CS.AI
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ArXi:2605.11841v1 Announce Type: cross Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theoretical properties remain incompletely understood. We adopt a spectral perspective on these algorithms, with two main contributions. First, we derive minimax-optimal convergence for RF regression, showing that, under mild regularity conditions on tree growth, the eigenvalue decay of the induced kernel operator governs the statistical rate.