Conference proceeding
Practical Contextual Bandits with Feedback Graphs
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2023), Vol.36
Advances in Neural Information Processing Systems
01/01/2023
Abstract
While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which interpolates between the full information and bandit regimes, provides a promising framework to mitigate the statistical complexity of learning. In this paper, we propose and analyze an approach to contextual bandits with feedback graphs based upon reduction to regression. The resulting algorithms are computationally practical and achieve established minimax rates, thereby reducing the statistical complexity in real-world applications.
Details
- Title: Subtitle
- Practical Contextual Bandits with Feedback Graphs
- Creators
- Mengxiao Zhang - Univ Southern Calif, Los Angeles, CA 90007 USAYuheng Zhang - University of Illinois Urbana-ChampaignOlga Vrousgou - Microsoft Res, Redmond, WA USAHaipeng Luo - Univ Southern Calif, Los Angeles, CA 90007 USAPaul Mineiro - Microsoft Res, Redmond, WA USA
- Contributors
- A Oh (Editor)T Neumann (Editor)A Globerson (Editor)K Saenko (Editor)M Hardt (Editor)S Levine (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2023), Vol.36
- Publisher
- Neural Information Processing Systems (Nips)
- Series
- Advances in Neural Information Processing Systems
- ISSN
- 1049-5258
- Number of pages
- 26
- Grant note
- IIS-1943607 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984701827302771
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