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arXiv Statistics ML By Naoki Awaya, Yuliang Xu, Li Ma

Two-sample comparison through additive tree models for density ratios

Read the original on arXiv Statistics ML →

The paper introduces additive tree models for estimating the density ratio between two distributions, using a novel balancing loss that enables training with supervised learning algorithms such as forward‑stagewise optimization and gradient boosting. The loss also functions as a pseudo‑likelihood, allowing generalized Bayesian inference via backfitting samplers for Bayesian additive regression trees, which provides uncertainty quantification. Experiments show that the boosting algorithms achieve high accuracy with low computational cost, and the Bayesian approach offers competitive accuracy while delivering uncertainty estimates, demonstrated on microbiome compositional data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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