强盗在线学习与预测的上下文
Yongyi Guo1, Ziping Xu2, Susan Murphy2
1Department of Statistics, University of Wisconsin-Madison, Madison, WI, 53706.
Proceedings of machine learning research
|October 28, 2024
概括
本研究介绍了一种新的在线算法,用于与杂的上下文数据相关的上下文盗问题. 新方法实现了线下遗憾,在没有观察到真实背景的场景中表现优于经典算法.
科学领域:
- 机器学习 机器学习
- 强化学习是一种强化学习.
- 统计决策理论 统计决策理论
背景情况:
- 语境盗问题对于在不确定性下进行连续决策至关重要.
- 经典的盗算法通常假定对上下文有完美的了解,这在许多应用中是不现实的.
- 当上下文信息杂或由另一个模型预测时,现有的方法失败,导致不消失的错误.
研究的目的:
- 开发第一个在线算法,能够在有噪音的上下文数据的上下文盗问题中实现线下遗憾.
- 为了解决设置,只有预测,而不是真实的,背景可用于决策.
- 在温和条件下为拟议的算法提供理论保证.
主要方法:
- 将经典的统计测量错误模型扩展到在线决策框架.
- 开发了一种新的在线算法,旨在处理依赖于杂上下文观测的政策.
- 理论分析以建立次线性遗憾保证.
主要成果:
- 拟议的算法实现了亚线性遗憾,在杂的上下文环境中比经典方法有了显著的改进.
- 即使上下文错误很大并且不会随着时间的推移而减少,算法也会证明它的有效性.
- 通过使用合成和现实世界的数字干预数据集进行模拟验证.
结论:
- 这种新的算法为在未观察到或预测到的环境下对上下文盗问题提供了强大的解决方案.
- 这项工作通过在这个具有挑战性的环境中为次线性遗憾提供了第一个理论保证,从而推动了这一领域的发展.
- 这种方法对依赖机器学习预测进行决策的各种应用具有实际意义.
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