Yasin Abbasi-Yadjkori, David Pál, and Csaba Szepesvári. Improved algorithms for linear stochastic bandits. Advances in neural information processing systems, 24, 2011.
Naoki Abe, Alan W Biermann, and Philip M Long. Reinforcement learning with immediate rewards and linear hypotheses. Algorithmica, 37:263-293, 2003.
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire. Taming the monster: A fast and simple algorithm for contextual bandits. In International Conference on Machine Learning, pages 1638-1646. PMLR, 2014.
Jean-Yves Audibert, Rémi Munos, and Csaba Szepesvári. Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. Theoretical Computer Science, 410(19):1876-1902, 2009.
Peter Auer. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3(Nov):397-422, 2002.
Alexandra Carpentier, Claire Vernade, and Yasin Abbasi-Yadkori. The elliptical potential lemma revisited. arXiv preprint arXiv:2010.10182, 2020.
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire. 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, 2011.
Varsha Dani, Thomas P Hayes, and Sham M Kakade. Stochastic linear optimization under bandit feedback. 2008.
Sarah Filippi, Olivier Cappe, Aurélien Garivier, and Csaba Szepesvári. Parametric bandits: The generalized linear case. Advances in Neural Information Processing Systems, 23, 2010.
Nima Hamidi and Mohsen Bayati. The elliptical potential lemma for general distributions with an application to linear thompson sampling. Operations Research, 2022.
F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1-19, 2015.
Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, and Rebecca Willett. Scalable generalized linear bandits: Online computation and hashing. Advances in Neural Information Processing Systems, 30, 2017.
Tze Leung Lai and Ching Zong Wei. Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems. The Annals of Statistics, 10(1): 154-166, 1982.
Tor Lattimore and Csaba Szepesvári. Bandit algorithms. Cambridge University Press, 2020.
Lihong Li, Wei Chu, John Langford, and Robert E Schapire. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web, pages 661-670, 2010.
Lihong Li, Yu Lu, and Dengyong Zhou. Provably optimal algorithms for generalized linear contextual bandits. In International Conference on Machine Learning, pages 2071-2080. PMLR, 2017.
Yingkai Li, Yining Wang, and Yuan Zhou. Nearly minimax-optimal regret for linearly parameterized bandits. In Conference on Learning Theory, pages 2173-2174. PMLR, 2019.
Tyler Lu, David Pál, and Martin Pál. Contextual multi-armed bandits. In Proceedings of the Thirteenth international conference on Artificial Intelligence and Statistics, pages 485-492. JMLR Workshop and Conference Proceedings, 2010.
Alessandro Nuara, Francesco Trovo, Nicola Gatti, and Marcello Restelli. A combinatorial-bandit algorithm for the online joint bid/budget optimization of pay-per-click advertising campaigns. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.