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布莱克威尔平易近人，没有遗憾的学习是等价的Blackwell Approachability and No-Regret Learning are Equivalent |
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| 课程网址: | http://videolectures.net/colt2011_abernethy_learning/ |
| 主讲教师: | Jacob Abernethy |
| 开课单位: | 加州大学伯克利分校 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the celebrated Blackwell Approachability Theorem for two-player games with vector payoffs. Blackwell himself previously showed that the theorem implies the existence of a “no regret” algorithm for a simple online learning problem. We show that this relationship is in fact much stronger, that Blackwell’s result is equivalent to, in a very strong sense, the problem of regret minimization for Online Linear Optimization. We show that any algorithm for one such problem can be efficiently converted into an algorithm for the other. We provide one novel application of this reduction: the first efficient algorithm for calibrated forecasting. |
| 关 键 词: | 矢量型游戏; 布莱克威尔逼近定理; 线性优化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-28：nkq |
| 阅读次数: | 130 |