在线学习与博弈论Online Learning and Game Theory |
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课程网址: | http://videolectures.net/mlss05us_kalai_olgt/ |
主讲教师: | Adam Kalai |
开课单位: | 芝加哥丰田技术学院 |
开课时间: | 2007-02-25 |
课程语种: | 英语 |
中文简介: | 我们考虑在线学习及其与博弈论的关系。在一个在线决策问题中,如辛格的演讲中,人们通常会做出一系列决策,并在做出每个决策后立即收到反馈。早在20世纪50年代,博弈论者就为这些问题给出了算法,并给出了强烈的遗憾保证。在不做统计假设的情况下,这些算法可以保证执行几乎与最佳单一决策相同的性能,在这种情况下,最好的选择是事后诸葛亮。我们讨论了这些算法在复杂的学习问题中的应用,在这些问题中,人们得到的反馈很少。示例包括在线路由、在线投资组合选择、在线广告和在线数据结构。我们还讨论了零和博弈中纳什均衡的学习和一般两人博弈中相关均衡的学习。 |
课程简介: | We consider online learning and its relationship to game theory. In an online decision-making problem, as in Singer's lecture, one typically makes a sequence of decisions and receives feedback immediately after making each decision. As far back as the 1950's, game theorists gave algorithms for these problems with strong regret guarantees. Without making statistical assumptions, these algorithms were guaranteed to perform nearly as well as the best single decision, where the best is chosen with the benefit of hindsight. We discuss applications of these algorithms to complex learning problems where one receives very little feedback. Examples include online routing, online portfolio selection, online advertizing, and online data structures. We also discuss applications to learning Nash equilibria in zero-sum games and learning correlated equilibria in general two-player games. |
关 键 词: | 数学; 博弈论; 计算学习理论; 计算机科学; 机器学习; 在线学习 |
课程来源: | 视频讲座网 |
最后编审: | 2021-03-12:nkq |
阅读次数: | 48 |