[1] Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems, pages 2312-2320, 2011.
[2] R. Agrawal, M. Hegde, D. Teneketzis, et al. Multi-armed bandit problems with multiple plays and switching cost. Stochastics and Stochastic reports, 29(4):437-459, 1990.
[3] A. Atsidakou, O. Papadigenopoulos, S. Basu, C. Caramanis, and S. Shakkottai. Combinatorial blocking bandits with stochastic delays. In International Conference on Machine Learning, pages 404-413. PMLR, 2021.
[4] J.-Y. Audibert, R. Munos, and C. Szepesvári. Tuning bandit algorithms in stochastic environments. In International conference on algorithmic learning theory, pages 150-165. Springer, 2007.
[5] J.-Y. Audibert, S. Bubeck, and G. Lugosi. Regret in online combinatorial optimization. Mathematics of Operations Research, 39(1):31-45, 2013.
[6] P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 32(1):48-77, 2002.
[7] A. Badanidiyuru, R. Kleinberg, and A. Slivkins. Bandits with knapsacks. Journal of the ACM (JACM), 65(3):1-55, 2018.
[8] S. Basu, R. Sen, S. Sanghavi, and S. Shakkottai. Blocking bandits. Advances in Neural Information Processing Systems, 32:4785-4794, 2019.
[9] S. Basu, O. Papadigenopoulos, C. Caramenis, and S. Shakkottai. Contextual blocking bandits. In International Conference on Artificial Intelligence and Statistics, pages 271-279. PMLR, 2021.
[10] N. Bishop, H. Chan, D. Mandal, and L. Tran-Thanh. Adversarial blocking bandits. Advances in Neural Information Processing Systems, 33:8139-8149, 2020.
[11] S. Cayci, A. Eryilmaz, and R. Srikant. Learning to control renewal processes with bandit feedback. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 3(2): 1-32, 2019.
[12] S. Cayci, A. Eryilmaz, and R. Srikant. Budget-constrained bandits over general cost and reward distributions. In International Conference on Artificial Intelligence and Statistics, pages 4388-4398. PMLR, 2020.
[13] N. Cesa-Bianchi, C. Gentile, and Y. Mansour. Delay and cooperation in nonstochastic bandits. Journal of Machine Learning Research, 20(17):1-38, 2019.
[14] J. P. Dickerson, K. A. Sankararaman, A. Srinivasan, and P. Xu. Allocation problems in ride-sharing platforms: Online matching with offline reusable resources. ACM Transactions on Economics and Computation, 9(3), June 2021.
[15] W. Ding, T. Qin, X.-D. Zhang, and T.-Y. Liu. Multi-armed bandit with budget constraint and variable costs. In Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.
[16] Z. Dong, S. Das, P. Fowler, and C.-J. Ho. Efficient nonmyopic online allocation of scarce reusable resources. In International Conference on Autonomous Agents and Multiagent Systems, pages 447-455, 2021.
[17] J. Edmonds. Matroids and the greedy algorithm. Mathematical programming, 1(1):127-136, 1971.
[18] Y. Gai, B. Krishnamachari, and R. Jain. Learning multiuser channel allocations in cognitive radio networks: A combinatorial multi-armed bandit formulation. In 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), pages 1–9. IEEE, 2010.