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The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters.
T. L. Lai and H. Robbins, “Asymptotically efficient adaptive allocation rules,” Advances in applied mathematics , vol. 6, no. 1, pp. 4–22, 1985
1985
Earlier work this paper cites.
V. Anantharam, P. Varaiya, and J. Walrand, “Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-part i: Iid rewards,” IEEE Transactions on Automatic Control , vol. 32, no. 11, pp. 968–976, 1987
1987
Earlier work this paper cites.
T. Rappaport, Wireless Communications: Principles and Practice , 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 2001
2001
Earlier work this paper cites.
P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,” Machine learning , vol. 47, no. 2-3, pp. 235–256, 2002
2002
Earlier work this paper cites.
X. Liu, E. K. Chong, and N. B. Shroff, “A framework for opportunistic scheduling in wireless networks,” Computer networks , vol. 41, no. 4, pp. 451–474, 2003
2003
Earlier work this paper cites.
I. H. Hou, V. Borkar, and P. R. Kumar, “A theory of qos for wireless,” in Proceedings of IEEE INFOCOM , April 2009, pp. 486–494
2009
Earlier work this paper cites.
V. Kanade, H. B. McMahan, and B. Bryan, “Sleeping experts and bandits with stochastic action availability and adversarial rewards,” in Artificial Intelligence and Statistics , 2009, pp. 272–279
2009
Earlier work this paper cites.
T. Lan, D. Kao, M. Chiang, and A. Sabharwal, “An axiomatic theory of fairness in network resource allocation,” in 2010 Proceedings IEEE INFOCOM , March 2010, pp. 1–9
2010
Earlier work this paper cites.
R. Kleinberg, A. Niculescu-Mizil, and Y. Sharma, “Regret bounds for sleeping experts and bandits,” Machine learning , vol. 80, no. 2-3, pp. 245–272, 2010
2010
Earlier work this paper cites.
M. J. Neely, “Stochastic network optimization with application to communication and queueing systems,” Synthesis Lectures on Communication Networks , vol. 3, no. 1, pp. 1–211, 2010
2010
Earlier work this paper cites.
J. Gittins, K. Glazebrook, and R. Weber, Multi-armed bandit allocation indices . John Wiley & Sons, 2011
2011
Earlier work this paper cites.
S. Bubeck and N. Cesa-Bianchi, “Regret analysis of stochastic and nonstochastic multi-armed bandit problems,” Foundations and Trends® in Machine Learning , vol. 5, no. 1, pp. 1–122, 2012
2012
Earlier work this paper cites.
Y. Gai, B. Krishnamachari, and R. Jain, “Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations,” IEEE/ACM Transactions on Networking (TON) , vol. 20, no. 5, pp. 1466–1478, 2012
2012
Cited alongside, same era.
W. Chen, Y. Wang, and Y. Yuan, “Combinatorial multi-armed bandit: General framework and applications,” in International Conference on Machine Learning , 2013, pp. 151–159
2013
Cited alongside, same era.
E. V. Denardo, E. A. Feinberg, and U. G. Rothblum, “The multi-armed bandit, with constraints,” Annals of Operations Research , vol. 208, no. 1, pp. 37–62, 2013
2013
Cited alongside, same era.
B. Kveton, Z. Wen, A. Ashkan, H. Eydgahi, and B. Eriksson, “Matroid bandits: Fast combinatorial optimization with learning,” in Proceedings of UAI , 2014
2014
Cited alongside, same era.
A. Chatterjee, G. Ghalme, S. Jain, R. Vaish, and Y. Narahari, “Analysis of thompson sampling for stochastic sleeping bandits.” in UAI , 2017
2017
Later among the works it cites.
L. Chen, J. Xu, and Z. Lu, “Contextual combinatorial multi-armed bandits with volatile arms and submodular reward,” in Advances in Neural Information Processing Systems , 2018, pp. 3247–3256
2018
Later among the works it cites.
A. Badanidiyuru, R. Kleinberg, and A. Slivkins, “Bandits with knapsacks,” Journal of the ACM (JACM) , vol. 65, no. 3, pp. 13:1–13:55, 2018
2018
Later among the works it cites.
K. Cai, X. Liu, Y. J. Chen, and J. C. S. Lui, “An online learning approach to network application optimization with guarantee,” in Proceedings of IEEE INFOCOM , 2018, in press
2018
Later among the works it cites.
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V. Kanade and T. Steinke, “Learning hurdles for sleeping experts,” ACM Transactions on Computation Theory (TOCT) , vol. 6, no. 3, p. 11, 2014
2014
Cited alongside, same era.
A. Krause and D. Golovin, “Submodular function maximization,” in Tractability: Practical Approaches to Hard Problems . Cambridge University Press, February 2014
2014
Cited alongside, same era.
R. Combes, M. S. T. M. Shahi, A. Proutiere et al. , “Combinatorial bandits revisited,” in Advances in Neural Information Processing Systems , 2015, pp. 2116–2124
2015
Cited alongside, same era.
R. Combes, C. Jiang, and R. Srikant, “Bandits with budgets: Regret lower bounds and optimal algorithms,” ACM SIGMETRICS Performance Evaluation Review , vol. 43, no. 1, pp. 245–257, 2015
2015
Cited alongside, same era.
W. Chen, W. Hu, F. Li, J. Li, Y. Liu, and P. Lu, “Combinatorial multi-armed bandit with general reward functions,” in Advances in Neural Information Processing Systems , 2016, pp. 1659–1667
2016
Cited alongside, same era.
M. Joseph, M. Kearns, J. H. Morgenstern, and A. Roth, “Fairness in learning: Classic and contextual bandits,” in Advances in Neural Information Processing Systems , 2016, pp. 325–333
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2018
Later among the works it cites.
M. S. Talebi and A. Proutiere, “Learning proportionally fair allocations with low regret,” Proceedings of the ACM on Measurement and Analysis of Computing Systems , vol. 2, no. 2, pp. 36:1–36:31, 2018
2018
Later among the works it cites.
W.-K. Hsu, J. Xu, X. Lin, and M. R. Bell, “Integrate learning and control in queueing systems with uncertain payoffs,” Purdue University, available at https://engineering.purdue.edu/%7elinx/papers.html, Tech. Rep., 2018
2018
Later among the works it cites.
“Adspeed ad server,” https://www.adspeed.com/, 2018, [Accessed: 2018-06-30]
2018
Later among the works it cites.
2018
Later among the works it cites.
F. Li, J. Liu, and B. Ji, “Combinatorial sleeping bandits with fairness constraints,” in Proceedings of IEEE INFOCOM , 2019, pp. 1–9
2019
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