Bandit Algorithms

Lattimore, Tor, Szepesvári, Csaba

  • 出版商: Cambridge
  • 出版日期: 2020-07-16
  • 售價: $2,130
  • 貴賓價: 9.8$2,087
  • 語言: 英文
  • 頁數: 536
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1108486827
  • ISBN-13: 9781108486828
  • 相關分類: Algorithms-data-structures
  • 立即出貨

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商品描述

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

商品描述(中文翻譯)

在機器學習中,面對不確定性的決策是一個重大挑戰,而多臂搶劫模型是一個常用的框架來應對這個挑戰。這本全面而嚴謹的介紹多臂搶劫問題的書籍探討了所有主要的情境,包括隨機、對抗和貝葉斯框架。著重於數學直覺和精心編寫的證明,使其成為已建立的研究人員的優秀參考資料,也是計算機科學、工程學、統計學、應用數學和經濟學研究生的有用資源。線性搶劫模型作為應用中最有用的模型之一,受到特別關注,而其他章節則專門討論組合搶劫、排名、非穩定問題、湯普森抽樣和純探索。書籍以對部分監控和馬可夫決策過程中的學習的介紹,為讀者展示了搶劫之外的世界。