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Online learning in opportunistic spectrum access: A restless bandit approach.

, and . INFOCOM, page 2462-2470. IEEE, (2011)

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Learning traffic congestion by contextual bandit problems for optimum localization., , , and . SIU, page 1-4. IEEE, (2017)Online learning in decentralized multi-user spectrum access with synchronized explorations., and . MILCOM, page 1-6. IEEE, (2012)A systematic learning method for optimal jamming., , , and . ICC, page 2822-2827. IEEE, (2015)Contextual Online Learning for Multimedia Content Aggregation., and . IEEE Trans. Multim., 17 (4): 549-561 (2015)Adaptive Contextual Learning for Unit Commitment in Microgrids With Renewable Energy Sources., , , and . IEEE J. Sel. Top. Signal Process., 12 (4): 688-702 (2018)Discovering, Learning and Exploiting Relevance., and . NIPS, page 1233-1241. (2014)Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness., , , and . NeurIPS, page 15243-15252. (2019)Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning., , , and . ICLR, OpenReview.net, (2021)Vector Optimization with Stochastic Bandit Feedback., and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 2165-2190. PMLR, (2023)Performance and Convergence of Multi-user Online Learning., and . GAMENETS, volume 75 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 321-336. Springer, (2011)