{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:13:19Z","timestamp":1762956799564,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006151, 62076161, 62106213, 72150002"],"award-info":[{"award-number":["62006151, 62076161, 62106213, 72150002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498413","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"219-229","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Combinatorial Bandits under Strategic Manipulations"],"prefix":"10.1145","author":[{"given":"Jing","family":"Dong","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}]},{"given":"Ke","family":"Li","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}]},{"given":"Shuai","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Baoxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1","article-title":"Best arm identification for contaminated bandits","volume":"20","author":"Altschuler Jason","year":"2019","unstructured":"Jason Altschuler, Victor-Emmanuel Brunel, and Alan Malek. 2019. Best arm identification for contaminated bandits. Journal of Machine Learning Research , Vol. 20, 91 (2019), 1--39.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_2_1","first-page":"11","article-title":"Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I. I. D. rewards","volume":"32","year":"1987","unstructured":"Venkatachalam. Anantharam, Pravin. Varaiya, and Jean. Walrand. 1987. Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I. I. D. rewards. IEEE Trans. Automat. Control , Vol. 32, 11 (Nov. 1987), 968--976.","journal-title":"IEEE Trans. Automat. Control"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1013689704352"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539701398375"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539701398375"},{"volume-title":"Bandit problems: Sequential allocation of experiments (Monographs on statistics and applied probability)","author":"Berry Donald A","key":"e_1_3_2_2_6_1","unstructured":"Donald A Berry and Bert Fristedt. 1985. Bandit problems: Sequential allocation of experiments (Monographs on statistics and applied probability) .Springer."},{"key":"e_1_3_2_2_7_1","volume-title":"Stochastic linear bandits robust to adversarial attacks . arXiv:2007.03285 [cs, stat] (Oct","author":"Bogunovic Ilija","year":"2020","unstructured":"Ilija Bogunovic, Arpan Losalka, Andreas Krause, and Jonathan Scarlett. 2020. Stochastic linear bandits robust to adversarial attacks . arXiv:2007.03285 [cs, stat] (Oct. 2020)."},{"volume-title":"Proceedings of the Thirty-Second Conference on Learning Theory","author":"Braverman Mark","key":"e_1_3_2_2_8_1","unstructured":"Mark Braverman, Jieming Mao, Jon Schneider, and S. Matthew Weinberg. 2019. Multi-armed bandit problems with strategic arms. In Proceedings of the Thirty-Second Conference on Learning Theory. Phoenix, USA."},{"volume-title":"Prediction, learning, and games","author":"Cesa-Bianchi Nicol\u00f2","key":"e_1_3_2_2_9_1","unstructured":"Nicol\u00f2 Cesa-Bianchi and G\u00e1bor Lugosi. 2006. Prediction, learning, and games .Cambridge University Press."},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning","author":"Chen Wei","year":"2013","unstructured":"Wei Chen, Yajun Wang, and Yang Yuan. 2013. Combinatorial multi-Armed bandit: general framework and applications. In Proceedings of the 30th International Conference on Machine Learning. Atlanta, Georgia, USA."},{"key":"e_1_3_2_2_11_1","volume-title":"Alexandre Prouti\u00e8re, and Marc Lelarge.","author":"Combes Richard","year":"2015","unstructured":"Richard Combes, Mohammad Sadegh Talebi, Alexandre Prouti\u00e8re, and Marc Lelarge. 2015. Combinatorial bandits revisited. In Advances in Neural Information Processing Systems. Montreal, Quebec, Canada., 2116--2124."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525228"},{"key":"e_1_3_2_2_13_1","volume-title":"Adversarial attacks on linear contextual bandits . arXiv:2002.03839 [cs, stat] (Oct","author":"Garcelon Evrard","year":"2020","unstructured":"Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, and Matteo Pirotta. 2020. Adversarial attacks on linear contextual bandits . arXiv:2002.03839 [cs, stat] (Oct. 2020)."},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of the Thirty-Second Conference on Learning Theory","author":"Gupta Anupam","year":"2019","unstructured":"Anupam Gupta, Tomer Koren, and Kunal Talwar. 2019. Better algorithms for stochastic bandits with adversarial corruptions. In Proceedings of the Thirty-Second Conference on Learning Theory. Phoenix, USA."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. 1609--1610","author":"Jain Shweta","year":"2014","unstructured":"Shweta Jain, Sujit Gujar, Onno Zoeter, and Y Narahari. 2014. A quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning. In Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems. 1609--1610."},{"key":"e_1_3_2_2_17_1","unstructured":"Kwang-Sung Jun Lihong Li Yuzhe Ma and Jerry Zhu. 2018. Adversarial attacks on stochastic bandits. In Advances in Neural Information Processing Systems. Montreal Canada 3640--3649."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274366"},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics","author":"Kveton Branislav","year":"2015","unstructured":"Branislav Kveton, Wen Zheng, Azin Ashkan, and Csaba Szepesv\u00e1ri. 2015. Tight regret bounds for stochastic combinatorial semi-bandits. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics . San Diego, California, USA."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401"},{"key":"e_1_3_2_2_21_1","volume-title":"Online influence maximization under linear threshold model. arxiv","author":"Li Shuai","year":"2011","unstructured":"Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, and Wei Chen. 2020. Online influence maximization under linear threshold model. arxiv: 2011.06378 [cs.LG]"},{"key":"e_1_3_2_2_22_1","volume-title":"Proceedings of The 33rd International Conference on Machine Learning","author":"Li Shuai","year":"2016","unstructured":"Shuai Li, Baoxiang Wang, Shengyu Zhang, and Wei Chen. 2016. Contextual combinatorial cascading bandits. In Proceedings of The 33rd International Conference on Machine Learning. New York, New York, USA."},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Liu Fang","year":"2019","unstructured":"Fang Liu and Ness Shroff. 2019. Data poisoning attacks on stochastic bandits. In Proceedings of the 36th International Conference on Machine Learning. Long Beach, California, USA."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3188745.3188918"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 1345--1352","author":"Rangi Anshuka","year":"2018","unstructured":"Anshuka Rangi and Massimo Franceschetti. 2018. Multi-armed bandit algorithms for crowdsourcing systems with online estimation of workers' ability. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 1345--1352."},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of the 31st International Conference on Algorithmic Learning Theory","author":"Rejwan Idan","year":"2020","unstructured":"Idan Rejwan and Yishay Mansour. 2020. Top-k combinatorial bandits with full-bandit feedback. In Proceedings of the 31st International Conference on Algorithmic Learning Theory. San Diego, California, USA."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9904-1952-09620-8"},{"key":"e_1_3_2_2_28_1","volume-title":"Ahmed","author":"Rossi Ryan A.","year":"2015","unstructured":"Ryan A. Rossi and Nesreen K. Ahmed. 2015. The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI . http:\/\/networkrepository.com"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics . Playa Blanca, Lanzarote, Canary Islands.","author":"Sankararaman Karthik Abinav","year":"2018","unstructured":"Karthik Abinav Sankararaman and Aleksandrs Slivkins. 2018. Combinatorial semi-bandits with knapsacks. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics . Playa Blanca, Lanzarote, Canary Islands."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2747409"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2593670"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/25.3-4.285"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2014.04.005"},{"key":"e_1_3_2_2_34_1","volume-title":"International Conference on Machine Learning . PMLR, 5114--5122","author":"Wang Siwei","year":"2018","unstructured":"Siwei Wang and Wei Chen. 2018. Thompson sampling for combinatorial semi-bandits. In International Conference on Machine Learning . PMLR, 5114--5122."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10939"},{"key":"e_1_3_2_2_36_1","volume-title":"Proceedings of the 31st Conference on Learning Theory","author":"Wei Chen-Yu","year":"2018","unstructured":"Chen-Yu Wei and Haipeng Luo. 2018. More adaptive algorithms for adversarial bandits. In Proceedings of the 31st Conference on Learning Theory. Stockholm, Sweden."},{"key":"e_1_3_2_2_37_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Zimmert Julian","year":"2019","unstructured":"Julian Zimmert, Haipeng Luo, and Chen-Yu Wei. 2019. Beating stochastic and adversarial semi-bandits optimally and simultaneously. In Proceedings of the 36th International Conference on Machine Learning . Long Beach, California, USA."},{"key":"e_1_3_2_2_38_1","volume-title":"Near optimal adversarial attack on UCB bandits . arXiv:2008.09312 [cs, stat] (Aug","author":"Zuo Shiliang","year":"2020","unstructured":"Shiliang Zuo. 2020. Near optimal adversarial attack on UCB bandits . arXiv:2008.09312 [cs, stat] (Aug. 2020)."}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event AZ USA","acronym":"WSDM '22"},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498413","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:51Z","timestamp":1750191531000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":38,"alternative-id":["10.1145\/3488560.3498413","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3498413","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}