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] · 2026-10-07 15:12

研究人员开发了 RoBART，一种用于贝叶斯加性回归树 (BART) 的新方法。RoBART 引入了特定树旋转，允许在旋转坐标中进行轴对齐分割，同时保持叶节点不变。该方法旨在改进对与预测变量轴不对齐的复杂边界的近似，这是标准 BART 的一个常见挑战。所提出的方法使用 Metropolis-Hastings 算法联合优化旋转序列和切点，并包括对后验收缩率的理论保证，证明了 RoBART 能够适应标准 BART 可能无法适应的内在维度。 AI

影响引入了一种新方法，用于提高统计分析中回归树模型的准确性和适应性。

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-07 15:12

RoBART：具有特定树旋转的贝叶斯加性回归树

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 rotated coordinates and constant leaves. We jointly p…

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RoBART：具有特定树旋转的贝叶斯加性回归树

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