盗问题的PAC-Bayes边界:一项调查和实验性比较
IEEE transactions on pattern analysis and machine intelligence
|August 18, 2023
概括
PAC-贝叶斯理论为强盗问题提供了有原则的算法. 虽然对离线上下文盗有效,但PAC-贝叶斯在线盗算法显示宽松的遗憾边界,表明未来的研究方向.
科学领域:
- 机器学习 机器学习
- 决策理论 决策理论
- 强化学习是一种强化学习.
背景情况:
- PAC-贝叶斯 (可能大致正确 - 贝叶斯理论) 理论为导出具有性能保证的学习算法提供了一个框架.
- 在医疗保健和金融领域普遍存在的盗问题,需要有原则的决策算法.
- 尽管PAC-Bayes对盗问题的应用具有潜力,但仍未得到充分探索.
研究的目的:
- 为了调查PAC-Bayes边界的盗问题.
- 为了实验性地比较PAC-贝叶斯边界在强盗环境中的性能.
- 研究PAC-Bayes对于设计具有性能保证的算法的实用性.
主要方法:
- 对PAC-Bayes边界的文献调查适用于强盗问题.
- 对PAC-贝叶斯离线上下文盗算法的实验性评估.
- 对PAC-贝叶斯在线盗算法的实验性评估.
主要成果:
- 对于设计具有性能保证的线下盗算法,PAC-Bayes边界是有效的.
- 一个PAC-贝叶斯离线上下文强盗算法使用随机神经网络策略实现了竞争性奖励和非真空保证.
- 经过测试的PAC-贝叶斯在线盗算法显示了松散的累积遗憾边界.
结论:
- PAC-Bayes是开发具有强大的理论保证的线下盗算法的一个有价值的工具.
- 需要进一步的研究来改进PAC-贝叶斯在线盗算法及其遗憾界限.
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