The content discusses the development of UCCB as an optimism principle for contextual bandits, offering solutions for handling general function classes and large context spaces efficiently. The proposed algorithms are proven to be optimal and computationally efficient, extending to infinite-action settings with the use of an offline regression oracle. Key concepts include systematic analysis of confidence bounds in policy space, potential function perspective, and counterfactual action divergence.
Existing optimistic algorithms like UCB struggle with general function classes and large context spaces in contextual bandit settings. The introduction of UCCB aims to address these challenges by building confidence bounds in policy space instead of action space. This innovative approach offers provably optimal and computationally efficient solutions for contextual bandits.
The paper presents a novel principle called Upper Counterfactual Confidence Bounds (UCCB) that focuses on designing optimistic algorithms for contextual bandits with general function classes and large context spaces. By analyzing confidence bounds in policy space rather than action space, the proposed algorithms demonstrate optimality and computational efficiency.
Key highlights include the systematic analysis of confidence bounds in policy space, a potential function perspective to articulate effectiveness, and a novel framework for studying contextual bandits with infinite actions. These contributions provide efficient solutions for handling complex contexts in machine learning applications.
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arxiv.org
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by Yunbei Xu,As... ב- arxiv.org 03-12-2024
https://arxiv.org/pdf/2007.07876.pdfשאלות מעמיקות