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快速可扩展和准确的DAG发现使用最佳订单得分搜索和生长-收缩树木
Bryan Andrews1, Joseph Ramsey2, Rubén Sánchez-Romero3
1Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454.
Advances in neural information processing systems
|September 16, 2024
概括
我们介绍了最佳订单得分搜索 (BOSS),这是一种因果发现的新方法. BOSS有效地学习复杂的图形模型,在大数据集 (如大脑网络) 的准确性和速度上优于现有的算法.
科学领域:
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
- 因果推理因果推理
背景情况:
- 学习图形条件独立结构对于因果发现至关重要.
- 当前的算法在数百个高度连接的变量的可扩展性方面扎,例如fMRI脑网络分析.
研究的目的:
- 引入新的算法,最佳顺序得分搜索 (BOSS) 和成长-收缩树 (GSTs),用于学习指向非循环图 (DAG).
- 为了提高对大规模问题的因果发现算法的准确性和执行时间.
主要方法:
- BOSS算法贪地搜索了可变变量.
- 通过缓存分数,GSTs被用来有效地构建和评分DAG.
- 这些算法在模拟和真实静止状态fMRI数据上进行了测试.
主要成果:
- 在准确性和执行时间方面,BOSS展示了最先进的性能.
- 它在各种条件下优于各种组合式和梯度式学习算法.
- 对于分析复杂的fMRI数据集,BOSS被证明是非常实用的.
结论:
- BOSS和GST在学习因果发现的图形模型方面提供了显著的进步.
- 这些方法为分析高维数据提供了可扩展和准确的解决方案,包括fMRI的大脑网络.
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