English(EN) RoBART: Bayesian Additive Regression Trees with Tree-Specific Rotations

RoBART 通过旋转增强了贝叶斯加性回归树

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员引入了 RoBART，一种用于贝叶斯加性回归树 (BART) 的新方法。RoBART 通过引入特定树旋转来解决传统 BART 在近似复杂边界方面的局限性。该方法允许在旋转坐标系中进行轴对齐分割，从而提高后验收缩率并适应内在维度，特别是对于各向异性 Hölder 平滑函数。 AI

影响引入了一种更具适应性的回归树方法，有可能在复杂数据场景中提高性能。

排序理由该条目描述了 arXiv 上发布的一种新统计方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Jeongung Heo, Seonghyun Jeong · 2026-10-08 04:00

RoBART: 具有树特定旋转的贝叶斯加性回归树

arXiv:2610.10214v1 Announce Type: cross Abstract: Bayesian additive regression trees (BART) can require many splits to approximate boundaries misaligned with the predictor axes. RoBART assigns each tree a rotation shared by all internal nodes, retaining axis-aligned splits in rot…