对抗性的蒙特卡洛超级学习最佳预测程序
Alex Luedtke1, Incheoul Chung1, Oleg Sofrygin2
1Department of Statistics, University of Washington, USA.
概括
这项研究引入了一种新的游戏理论方法,用于元学习预测策略. 通过神经网络确定并实施了一种最优的预测策略,不变于特征和观测转换.
科学领域:
- 机器学习 机器学习
- 统计学学习理论
背景情况:
- 超级学习旨在优化预测程序.
- 标准方法通常对某些数据转换缺乏理论保证.
研究的目的:
- 将超级学习视为自然和预测器之间的两人游戏.
- 为了推导出一个最佳的预测策略,具有理想的不变性和等差性质.
主要方法:
- 制定了超级学习作为一个游戏,先验数据分布.
- 建立了最佳预测器策略的条件.
- 介绍了一个包含这些属性的神经网络架构.
主要成果:
- 确定了一个最优的预测器策略,不变于特征/观测 permutations 和shifts/rescalings.
- 在实验中,与标准方法相比,表现良好.
- 提出了一个满足衍生性质的神经网络架构.
结论:
- 游戏理论框架为meta-learning提供了一个原则性的方法.
- 拟议的神经网络战略提供了更好的性能和理论依据.
- 这项工作促进了对强大且可适应的预测模型的理解.
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