Variance-Adaptive Algorithm for Probabilistic Maximum Coverage Bandits with General Feedback
Author:
Affiliation:
1. The Chinese University of Hong Kong
2. Carnegie Mellon University
3. Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Science
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10228851/10228852/10228940.pdf?arnumber=10228940
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1. An analysis of approximations for maximizing submodular set functions—I
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5. Tight lower bounds for combinatorial multi-armed bandits;merlis;Conference on Learning Theory,2020
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1. Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive Approach;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20
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