Online allocation with risk information

Shigeaki Harada, Eiji Takimoto, Akira Maruoka

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

We consider the problem of dynamically apportioning resources among a set of options in a worst-case online framework. The model we investigate is a generalization of the well studied online learning model. In particular, we allow the learner to see as additional information how high the risk of each option is. This assumption is natural in many applications like horse-race betting, where gamblers know odds for all options before placing bets. We apply the Aggregating Algorithm to this problem and give a tight performance bound. The results support our intuition that we should bet more on low-risk options. Surprisingly, however, the Hedge Algorithm without seeing risk information performs nearly as well as the Aggregating Algorithm. So the risk information does not help much. Moreover, the loss bound does not depend on the values of relatively small risks.

本文言語英語
ホスト出版物のタイトルAlgorithmic Learning Theory - 16th International Conference, ALT 2005, Proceedings
ページ343-355
ページ数13
DOI
出版ステータス出版済み - 12 1 2005
外部発表はい
イベント16th International Conference on Algorithmic Learning Theory, ALT 2005 - Singapore, シンガポール
継続期間: 10 8 200510 11 2005

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3734 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他16th International Conference on Algorithmic Learning Theory, ALT 2005
国/地域シンガポール
CitySingapore
Period10/8/0510/11/05

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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