学习缺少数据的不变表示
Mark Goldstein1, Aahlad Puli1, Rajesh Ranganath1
1New York University.
Proceedings of machine learning research
|August 30, 2023
概括
这项研究引入了新的方法,使机器学习模型能够在新数据上可靠地执行,即使在训练过程中缺少一些信息,如人口统计数据. 这些技术通过解决虚假的相关性来提高预测准确性.
科学领域:
- 机器学习 机器学习
- 因果推理因果推理
- 数据科学数据科学数据科学
背景情况:
- 机器学习模型中的虚假相关性导致尽管训练表现良好,但对测试群体的概括性差.
- 不变性原理,强制执行与麻烦变量的独立性,为模型测试性能提供理论保证.
- 烦变量 (例如,人口统计学,背景标签) 在培训期间往往没有被观察到,这限制了这些保证的应用.
研究的目的:
- 在机器学习中开发方法来强制执行在机器学习中缺失的麻烦变量中的独立性.
- 导出最大平均差异 (MMD) 估计器不变性目标,当麻烦数据是不完整的.
- 在模拟和真实世界的临床数据上评估这些估计器的有效性.
主要方法:
- 根据缺少的麻烦数据,根据不变性目标量身定制的新型MMD估计器的推导.
- 使用这些衍生MMD估计器的模型优化.
- 在模拟数据集和临床数据上进行实证验证,以评估性能.
主要成果:
- 建议的MMD估计器有效地处理缺失的麻烦变量.
- 通过这些估计进行优化,可以将测试性能与使用完整干扰数据的方法相提并论.
- 在模拟环境和临床环境中证明了稳定性和适用性.
结论:
- 开发的MMD估计器为在不完全的麻烦信息下实现模型不变性提供了实际解决方案.
- 这种方法在现实场景中增强了模型的概括性和可靠性,在现实场景中,数据通常部分缺失.
- 这些发现表明,实现更强大,更值得信赖的机器学习应用程序是一个可行的途径,特别是在医疗保健等敏感领域.
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