Abbasi-Yadkori, Y., Pál, D., and Szepesváři, C. (2011). Improved algorithms for linear stochastic bandits. Advances in neural information processing systems, 24.
Auer, P. (2002). Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3(Nov):397-422.
Cesa-Bianchi, N., Gentile, C., and Zappella, G. (2013). A gang of bandits. Advances in neural information processing systems, 26.
Chu, W., Li, L., Reyzin, L., and Schapire, R. (2011). Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 208-214. JMLR Workshop and Conference Proceedings.
Dani, V., Hayes, T. P., and Kakade, S. M. (2008). Stochastic linear optimization under bandit feedback. 21st Annual Conference on Learning Theory, pages 355-366.
Dubey, A. and Pentland, A. (2020). Differentially-private federated linear bandits. Advances in Neural Information Processing Systems, 33:6003-6014.
Han, Y., Zhou, Z., Zhou, Z., Blanchet, J., Glynn, P. W., and Ye, Y. (2020). Sequential batch learning in finite-action linear contextual bandits. arXiv preprint arXiv:2004.06321.
Harper, F. M. and Konstan, J. A. (2015). The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst., 5(4):1-19.
He, J., Wang, T., Min, Y., and Gu, Q. (2022a). A simple and provably efficient algorithm for asynchronous federated contextual linear bandits. arXiv preprint arXiv:2207.03106.
He, J., Zhou, D., Zhang, T., and Gu, Q. (2022b). Nearly optimal algorithms for linear contextual bandits with adversarial corruptions. Advances in neural information processing systems.
Huang, R., Wu, W., Yang, J., and Shen, C. (2021). Federated linear contextual bandits. Advances in Neural Information Processing Systems, 34:27057-27068.
Lattimore, T. and Szepesvári, C. (2020). Bandit algorithms. Cambridge University Press.
Li, C. and Wang, H. (2022a). Asynchronous upper confidence bound algorithms for federated linear bandits. In International Conference on Artificial Intelligence and Statistics, pages 6529-6553. PMLR.
Li, C. and Wang, H. (2022b). Communication efficient federated learning for generalized linear bandits. arXiv preprint arXiv:2202.01087.
Li, L., Chu, W., Langford, J., and Schapire, R. E. (2010). A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web, pages 661-670.
Li, T., Song, L., and Fragouli, C. (2020). Federated recommendation system via differential privacy. In 2020 IEEE International Symposium on Information Theory (ISIT), pages 2592-2597. IEEE.
Li, Y., Wang, Y., and Zhou, Y. (2019). Nearly minimax-optimal regret for linearly parameterized bandits. In Conference on Learning Theory, pages 2173-2174. PMLR.
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proc. AISTATS, pages 1273-1282, Fort Lauderdale, FL, USA.
Ruan, Y., Yang, J., and Zhou, Y. (2021). Linear bandits with limited adaptivity and learning distributional optimal design. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, pages 74-87.
Salgia, S. and Zhao, Q. (2023). Distributed linear bandits under communication constraints. In International Conference on Machine Learning, pages 29845-29875. PMLR.