[5] Branislav Kveton, Csaba Szepesvari, Zheng Wen, and Azin Ashkan. Cascading bandits: Learning to rank in the cascade model. In International conference on machine learning, pages 767-776. PMLR, 2015.
[6] Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pages 129-136, 2007.
[7] Tie-Yan Liu et al. Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3):225-331, 2009.
[8] Fatih Cakir, Kun He, Xide Xia, Brian Kulis, and Stan Sclaroff. Deep metric learning to rank. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1861-1870, 2019.
[9] Kwang-Sung Jun, Lihong Li, Yuzhe Ma, and Jerry Zhu. Adversarial attacks on stochastic bandits. Advances in Neural Information Processing Systems, 31, 2018.
[10] Fang Liu and Ness Shroff. Data poisoning attacks on stochastic bandits. In International Conference on Machine Learning, pages 4042-4050. PMLR, 2019.
[11] Yinglun Xu, Bhuvesh Kumar, and Jacob D Abernethy. Observation-free attacks on stochastic bandits. Advances in Neural Information Processing Systems, 34:22550-22561, 2021.
[12] Sébastien Bubeck, Nicolo Cesa-Bianchi, et al. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning, 5(1):1-122, 2012.
[13] Sougata Chaudhuri and Ambuj Tewari. Online learning to rank with top-k feedback. The Journal of Machine Learning Research, 18(1):3599-3648, 2017.
[14] Junpei Komiyama, Junya Honda, and Akiko Takeda. Position-based multiple-play bandit problem with unknown position bias. Advances in Neural Information Processing Systems, 30, 2017.
[15] Beyza Ermis, Patrick Ernst, Yannik Stein, and Giovanni Zappella. Learning to rank in the position based model with bandit feedback. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 2405–2412, 2020.
[16] Branislav Kveton, Zheng Wen, Azin Ashkan, and Csaba Szepesvari. Combinatorial cascading bandits. Advances in Neural Information Processing Systems, 28, 2015.
[17] Shi Zong, Hao Ni, Kenny Sung, Nan Rosemary Ke, Zheng Wen, and Branislav Kveton. Cascading bandits for large-scale recommendation problems. Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016.
[18] Shuai Li, Baoxiang Wang, Shengyu Zhang, and Wei Chen. Contextual combinatorial cascading bandits. In International conference on machine learning, pages 1245-1253. PMLR, 2016.
[19] Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, and R Srikant. Minimax regret for cascading bandits. Advances in Neural Information Processing Systems, 2022.
[20] Xutong Liu, Jinhang Zuo, Siwei Wang, John CS Lui, Mohammad Hajiesmaili, Adam Wierman, and Wei Chen. Contextual combinatorial bandits with probabilistically triggered arms. In International Conference on Machine Learning, pages 22559-22593. PMLR, 2023.
[21] Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, and Zheng Wen. Dcm bandits: Learning to rank with multiple clicks. In International Conference on Machine Learning, pages 1215-1224. PMLR, 2016.
[22] Weiwen Liu, Shuai Li, and Shengyu Zhang. Contextual dependent click bandit algorithm for web recommendation. In Computing and Combinatorics: 24th International Conference, COCOON 2018, Proceedings 24, pages 39-50. Springer, 2018.