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arXiv · 2406.00660

Non-asymptotic Properties of Generalized Mondrian Forests in Statistical Learning

Haoran Zhan·Jingli Wang·Yingcun Xia
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Abstract

Random Forests have been extensively used in regression and classification, inspiring the development of various forest-based methods. Among these, Mondrian Forests, derived from the Mondrian process, mark a significant advancement. Expanding on Mondrian Forests, this paper presents a general framework for statistical learning, encompassing a range of common learning tasks such as least squares regression, $\ell_1$ regression, quantile regression, and classification. Under mild assumptions on the loss functions, we provide upper bounds on the regret/risk functions for the estimators and demonstrate their statistical consistency.

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Haoran Zhan, Jingli Wang, Yingcun Xia. 2024-06-02. Non-asymptotic Properties of Generalized Mondrian Forests in Statistical Learning. https://arxiv.org/abs/2406.00660

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