{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T04:50:46Z","timestamp":1780635046750,"version":"3.54.1"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["SRFS2122-4S02, PDFS2324-4S04"],"award-info":[{"award-number":["SRFS2122-4S02, PDFS2324-4S04"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nation Science Foundation of China","award":["62306138"],"award-info":[{"award-number":["62306138"]}]},{"DOI":"10.13039\/501100006374","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2325956, CAREER2045641, CPS-2136199, CNS-2102963, and CNS-2106299"],"award-info":[{"award-number":["CNS-2325956, CAREER2045641, CPS-2136199, CNS-2102963, and CNS-2106299"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Army Research Laboratory","doi-asserted-by":"publisher","award":["W911NF-17-2-0196"],"award-info":[{"award-number":["W911NF-17-2-0196"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Innovation Program of State Key Laboratory for Novel Software Technology at Nanjing University","award":["ZZKT2024B15"],"award-info":[{"award-number":["ZZKT2024B15"]}]},{"name":"JiangsuNSF","award":["BK20230784"],"award-info":[{"award-number":["BK20230784"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2025,3,6]]},"abstract":"<jats:p>We study the cooperative asynchronous multi-agent multi-armed bandits problem, where each agent's active (arm pulling) decision rounds are asynchronous. That is, in each round, only a subset of agents is active to pull arms, and this subset is unknown and time-varying. We consider two models of multi-agent cooperation, fully distributed and leader-coordinated, and propose algorithms for both models that attain near-optimal regret and communications bounds, both of which are almost as good as their synchronous counterparts. The fully distributed algorithm relies on a novel communication policy consisting of accuracy adaptive and on-demand components, and successive arm elimination for decision-making. For leader-coordinated algorithms, a single leader explores arms and recommends them to other agents (followers) to exploit. As agents' active rounds are unknown, a competent leader must be chosen dynamically. We propose a variant of the Tsallis-INF algorithm with low switches to choose such a leader sequence. Lastly, we report numerical simulations of our new asynchronous algorithms with other known baselines.<\/jats:p>","DOI":"10.1145\/3711696","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T16:05:24Z","timestamp":1741622724000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Asynchronous Multi-Agent Bandits: Fully Distributed\n            <i>vs<\/i>\n            . Leader-Coordinated Algorithms"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8043-8521","authenticated-orcid":false,"given":"Xuchuang","family":"Wang","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1420-9214","authenticated-orcid":false,"given":"Yu-Zhen Janice","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8628-5873","authenticated-orcid":false,"given":"Xutong","family":"Liu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9056-0500","authenticated-orcid":false,"given":"Lin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology &amp; National Key Laboratory for Novel Software Technology, Nanjing University, Suzhou, Jinagsu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-2254","authenticated-orcid":false,"given":"Mohammad","family":"Hajiesmaili","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7808-7375","authenticated-orcid":false,"given":"Don","family":"Towsley","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-0384","authenticated-orcid":false,"given":"John C.S.","family":"Lui","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2006.05.001"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2020.1052"},{"key":"e_1_2_1_3_1","volume-title":"Online Bandit Learning against an Adaptive Adversary: From Regret to Policy Regret (ICML'12)","author":"Arora Raman","unstructured":"Raman Arora, Ofer Dekel, and Ambuj Tewari. 2012. Online Bandit Learning against an Adaptive Adversary: From Regret to Policy Regret (ICML'12). Omnipress, Madison, WI, USA, 1747--1754."},{"key":"e_1_2_1_4_1","unstructured":"Jean-Yves Audibert and S\u00e9bastien Bubeck. 2009. Minimax policies for adversarial and stochastic bandits. In COLT. 217--226."},{"key":"e_1_2_1_5_1","series-title":"SIAM journal on computing","volume-title":"The nonstochastic multiarmed bandit problem","author":"Auer Peter","year":"2002","unstructured":"Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. 2002. The nonstochastic multiarmed bandit problem. SIAM journal on computing, Vol. 32, 1 (2002), 48--77."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3546258.3546272"},{"key":"e_1_2_1_7_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Bar-On Yogev","year":"2019","unstructured":"Yogev Bar-On and Yishay Mansour. 2019. Individual regret in cooperative nonstochastic multi-armed bandits. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_2_1_8_1","volume-title":"Differential privacy for multi-armed bandits: What is it and what is its cost? arXiv preprint arXiv:1905.12298","author":"Basu Debabrota","year":"2019","unstructured":"Debabrota Basu, Christos Dimitrakakis, and Aristide Tossou. 2019. Differential privacy for multi-armed bandits: What is it and what is its cost? arXiv preprint arXiv:1905.12298 (2019)."},{"key":"e_1_2_1_9_1","unstructured":"Lilian Besson and Emilie Kaufmann. 2018. Multi-player bandits revisited. In Algorithmic Learning Theory. PMLR 56--92."},{"key":"e_1_2_1_10_1","volume-title":"What doubling tricks can and can't do for multi-armed bandits. arXiv preprint arXiv:1803.06971","author":"Besson Lilian","year":"2018","unstructured":"Lilian Besson and Emilie Kaufmann. 2018b. What doubling tricks can and can't do for multi-armed bandits. arXiv preprint arXiv:1803.06971 (2018)."},{"key":"e_1_2_1_11_1","first-page":"2016","article-title":"Cooperative multi-player bandit optimization","volume":"33","author":"Bistritz Ilai","year":"2020","unstructured":"Ilai Bistritz and Nicholas Bambos. 2020. Cooperative multi-player bandit optimization. Advances in Neural Information Processing Systems, Vol. 33 (2020), 2016--2027.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_12_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Bistritz Ilai","year":"2018","unstructured":"Ilai Bistritz and Amir Leshem. 2018. Distributed multi-player bandits-a game of thrones approach. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1287\/moor.2020.1051"},{"key":"e_1_2_1_14_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Boursier Etienne","year":"2019","unstructured":"Etienne Boursier and Vianney Perchet. 2019. SIC-MMAB: synchronisation involves communication in multiplayer multi-armed bandits. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_2_1_15_1","volume-title":"Conference on Learning Theory. PMLR, 961--987","author":"Bubeck S\u00e9bastien","year":"2020","unstructured":"S\u00e9bastien Bubeck, Yuanzhi Li, Yuval Peres, and Mark Sellke. 2020. Non-stochastic multi-player multi-armed bandits: Optimal rate with collision information, sublinear without. In Conference on Learning Theory. PMLR, 961--987."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218651"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485906"},{"key":"e_1_2_1_18_1","volume-title":"Kullback-Leibler upper confidence bounds for optimal sequential allocation. The Annals of Statistics","author":"Capp\u00e9 Olivier","year":"2013","unstructured":"Olivier Capp\u00e9, Aur\u00e9lien Garivier, Odalric-Ambrym Maillard, R\u00e9mi Munos, and Gilles Stoltz. 2013. Kullback-Leibler upper confidence bounds for optimal sequential allocation. The Annals of Statistics (2013), 1516--1541."},{"key":"e_1_2_1_19_1","unstructured":"Nicol\u00f2 Cesa-Bianchi Tommaso Cesari and Claire Monteleoni. 2020. Cooperative online learning: Keeping your neighbors updated. In Algorithmic learning theory. PMLR 234--250."},{"key":"e_1_2_1_20_1","volume-title":"Conference on Learning Theory. PMLR, 605--622","author":"Cesa-Bianchi Nicol'o","year":"2016","unstructured":"Nicol'o Cesa-Bianchi, Claudio Gentile, Yishay Mansour, and Alberto Minora. 2016. Delay and cooperation in non-stochastic bandits. In Conference on Learning Theory. PMLR, 605--622."},{"key":"e_1_2_1_21_1","volume-title":"Sanmay Das, and Brendan Juba.","author":"Chakraborty Mithun","year":"2017","unstructured":"Mithun Chakraborty, Kai Yee Phoebe Chua, Sanmay Das, and Brendan Juba. 2017. Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits.. In IJCAI. 164--170."},{"key":"e_1_2_1_22_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3471--3481","author":"Chawla Ronshee","year":"2020","unstructured":"Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, and Sanjay Shakkottai. 2020. The gossiping insert-eliminate algorithm for multi-agent bandits. In International Conference on Artificial Intelligence and Statistics. PMLR, 3471--3481."},{"key":"e_1_2_1_23_1","volume-title":"On-Demand Communication for Asynchronous Multi-Agent Bandits. In International Conference on Artificial Intelligence and Statistics. PMLR, 3903--3930","author":"Janice Chen Yu-Zhen","year":"2023","unstructured":"Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang, Xutong Liu, Mohammad Hajiesmaili, John C.S. Lui, and Don Towsley. 2023. On-Demand Communication for Asynchronous Multi-Agent Bandits. In International Conference on Artificial Intelligence and Statistics. PMLR, 3903--3930."},{"key":"e_1_2_1_24_1","volume-title":"Shuffle private linear contextual bandits. arXiv preprint arXiv:2202.05567","author":"Chowdhury Sayak Ray","year":"2022","unstructured":"Sayak Ray Chowdhury and Xingyu Zhou. 2022. Shuffle private linear contextual bandits. arXiv preprint arXiv:2202.05567 (2022)."},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 11th International Conference on Telecommunications. IEEE, 301--306","author":"Csurgai-Horv\u00e1th L\u00e1szl\u00f3","year":"2011","unstructured":"L\u00e1szl\u00f3 Csurgai-Horv\u00e1th and J\u00e1nos Bit\u00f3. 2011. Primary and secondary user activity models for cognitive wireless network. In Proceedings of the 11th International Conference on Telecommunications. IEEE, 301--306."},{"key":"e_1_2_1_26_1","unstructured":"Yuval Dagan and Crammer Koby. 2018. A better resource allocation algorithm with semi-bandit feedback. In Algorithmic Learning Theory. PMLR 268--320."},{"key":"e_1_2_1_27_1","volume-title":"International Conference on Machine Learning. PMLR, 2730--2739","author":"Abhimanyu","unstructured":"Abhimanyu Dubey et al. 2020. Cooperative multi-agent bandits with heavy tails. In International Conference on Machine Learning. PMLR, 2730--2739."},{"key":"e_1_2_1_28_1","first-page":"6003","article-title":"Differentially-private federated linear bandits","volume":"33","author":"Dubey Abhimanyu","year":"2020","unstructured":"Abhimanyu Dubey and AlexSandy' Pentland. 2020. Differentially-private federated linear bandits. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6003--6014.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_29_1","volume-title":"Foundations and Trends\u00ae in Theoretical Computer Science","volume":"9","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science, Vol. 9, 3--4 (2014), 211--407."},{"key":"e_1_2_1_30_1","volume-title":"International Conference on Machine Learning. PMLR","author":"F\u00e9raud Rapha\u00ebl","year":"2019","unstructured":"Rapha\u00ebl F\u00e9raud, R\u00e9da Alami, and Romain Laroche. 2019. Decentralized exploration in multi-armed bandits. In International Conference on Machine Learning. PMLR, 1901--1909."},{"key":"e_1_2_1_31_1","volume-title":"A simple and provably efficient algorithm for asynchronous federated contextual linear bandits. Advances in neural information processing systems","author":"He Jiafan","year":"2022","unstructured":"Jiafan He, Tianhao Wang, Yifei Min, and Quanquan Gu. 2022. A simple and provably efficient algorithm for asynchronous federated contextual linear bandits. Advances in neural information processing systems, Vol. 35 (2022), 4762--4775."},{"key":"e_1_2_1_32_1","volume-title":"Advances in Neural Information Processing Systems","volume":"26","author":"Hillel Eshcar","year":"2013","unstructured":"Eshcar Hillel, Zohar S Karnin, Tomer Koren, Ronny Lempel, and Oren Somekh. 2013. Distributed exploration in multi-armed bandits. Advances in Neural Information Processing Systems, Vol. 26 (2013)."},{"key":"e_1_2_1_33_1","first-page":"20185","article-title":"Asynchronous decentralized online learning","volume":"34","author":"Jiang Jiyan","year":"2021","unstructured":"Jiyan Jiang, Wenpeng Zhang, Jinjie Gu, and Wenwu Zhu. 2021. Asynchronous decentralized online learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 20185--20196.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_34_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Joulani Pooria","year":"2019","unstructured":"Pooria Joulani, Andr\u00e1s Gy\u00f6rgy, and Csaba Szepesv\u00e1ri. 2019. Think out of the ''box'': Generically-constrained asynchronous composite optimization and hedging. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_2_1_35_1","volume-title":"Retrieved","year":"2015","unstructured":"Kaggle. 2015. Avito Context Ad Clicks. Retrieved Jul. 22, 2022 from https:\/\/www.kaggle.com\/c\/avito-context-ad-clicks"},{"key":"e_1_2_1_36_1","unstructured":"Kaggle. n.d.. Avito Context Ad Clicks. https:\/\/www.kaggle.com\/c\/avito-context-ad-clicks"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2852361"},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Tze Leung Lai Herbert Robbins et al. 1985. Asymptotically efficient adaptive allocation rules. Advances in applied mathematics Vol. 6 1 (1985) 4--22.","DOI":"10.1016\/0196-8858(85)90002-8"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2016.7798264"},{"key":"e_1_2_1_40_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 6529--6553","author":"Li Chuanhao","year":"2022","unstructured":"Chuanhao Li and Hongning Wang. 2022. Asynchronous upper confidence bound algorithms for federated linear bandits. In International Conference on Artificial Intelligence and Statistics. PMLR, 6529--6553."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.23919\/WiOpt56218.2022.9930524"},{"key":"e_1_2_1_42_1","first-page":"7813","article-title":"One more step towards reality: Cooperative bandits with imperfect communication","volume":"34","author":"Madhushani Udari","year":"2021","unstructured":"Udari Madhushani, Abhimanyu Dubey, Naomi Leonard, and Alex Pentland. 2021. One more step towards reality: Cooperative bandits with imperfect communication. Advances in Neural Information Processing Systems, Vol. 34 (2021), 7813--7824.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_43_1","volume-title":"A survey on mobile edge computing: The communication perspective","author":"Mao Yuyi","year":"2017","unstructured":"Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B Letaief. 2017. A survey on mobile edge computing: The communication perspective. IEEE communications surveys & tutorials, Vol. 19, 4 (2017), 2322--2358."},{"key":"e_1_2_1_44_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Mart\u00ednez-Rubio David","year":"2019","unstructured":"David Mart\u00ednez-Rubio, Varun Kanade, and Patrick Rebeschini. 2019. Decentralized cooperative stochastic bandits. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2017.2675901"},{"key":"e_1_2_1_46_1","volume-title":"Waleed Al-Herz, Safa Baris, Carolina Prando, Laszlo Rosivall, et al.","author":"Momtazmanesh Sara","year":"2020","unstructured":"Sara Momtazmanesh, Hans D Ochs, Lucina Q Uddin, Matjaz Perc, John M Routes, Duarte Nuno Vieira, Waleed Al-Herz, Safa Baris, Carolina Prando, Laszlo Rosivall, et al. 2020. All together to fight COVID-19. The American journal of tropical medicine and hygiene, Vol. 102, 6 (2020), 1181."},{"key":"e_1_2_1_47_1","volume-title":"Multi-armed bandits with local differential privacy. arXiv preprint arXiv:2007.03121","author":"Ren Wenbo","year":"2020","unstructured":"Wenbo Ren, Xingyu Zhou, Jia Liu, and Ness B Shroff. 2020. Multi-armed bandits with local differential privacy. arXiv preprint arXiv:2007.03121 (2020)."},{"key":"e_1_2_1_48_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 388--396","author":"Richard Hugo","year":"2024","unstructured":"Hugo Richard, Etienne Boursier, and Vianney Perchet. 2024. Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication. In International Conference on Artificial Intelligence and Statistics. PMLR, 388--396."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366701"},{"key":"e_1_2_1_50_1","volume-title":"Differentially private contextual linear bandits. arXiv preprint arXiv:1810.00068","author":"Shariff Roshan","year":"2018","unstructured":"Roshan Shariff and Or Sheffet. 2018. Differentially private contextual linear bandits. arXiv preprint arXiv:1810.00068 (2018)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17156"},{"key":"e_1_2_1_52_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Shi Chengshuai","year":"2021","unstructured":"Chengshuai Shi, Wei Xiong, Cong Shen, and Jing Yang. 2021. Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_2_1_53_1","volume-title":"International Conference on Machine Learning. PMLR, 19--27","author":"Szorenyi Balazs","year":"2013","unstructured":"Balazs Szorenyi, R\u00f3bert Busa-Fekete, Istv\u00e1n Hegedus, R\u00f3bert Orm\u00e1ndi, M\u00e1rk Jelasity, and Bal\u00e1zs K\u00e9gl. 2013. Gossip-based distributed stochastic bandit algorithms. In International Conference on Machine Learning. PMLR, 19--27."},{"key":"e_1_2_1_54_1","first-page":"24956","article-title":"Differentially private multi-armed bandits in the shuffle model","volume":"34","author":"Tenenbaum Jay","year":"2021","unstructured":"Jay Tenenbaum, Haim Kaplan, Yishay Mansour, and Uri Stemmer. 2021. Differentially private multi-armed bandits in the shuffle model. Advances in Neural Information Processing Systems, Vol. 34 (2021), 24956--24967.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10212"},{"key":"e_1_2_1_56_1","volume-title":"Cooperative path planning of unmanned aerial vehicles","author":"Tsourdos Antonios","unstructured":"Antonios Tsourdos, Brian White, and Madhavan Shanmugavel. 2010. Cooperative path planning of unmanned aerial vehicles. Vol. 32. John Wiley & Sons."},{"key":"e_1_2_1_57_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 4120--4129","author":"Wang Po-An","year":"2020","unstructured":"Po-An Wang, Alexandre Proutiere, Kaito Ariu, Yassir Jedra, and Alessio Russo. 2020b. Optimal algorithms for multiplayer multi-armed bandits. In International Conference on Artificial Intelligence and Statistics. PMLR, 4120--4129."},{"key":"e_1_2_1_58_1","volume-title":"Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. In 8th International Conference on Learning Representations, ICLR 2020","author":"Wang Yuanhao","year":"2020","unstructured":"Yuanhao Wang, Jiachen Hu, Xiaoyu Chen, and Liwei Wang. 2020a. Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020."},{"key":"e_1_2_1_59_1","volume-title":"Distributed Bandits with Heterogeneous Agents. In In Proceedings of The IEEE International Conference on Computer Communications","author":"Yang Lin","year":"2022","unstructured":"Lin Yang, Yu-Zhen Janice Chen, Mohammad Hajiesmaili, John C.S. Lui, and Don Towsley. 2022. Distributed Bandits with Heterogeneous Agents. In In Proceedings of The IEEE International Conference on Computer Communications 2022."},{"key":"e_1_2_1_60_1","first-page":"8885","article-title":"Cooperative stochastic bandits with asynchronous agents and constrained feedback","volume":"34","author":"Yang Lin","year":"2021","unstructured":"Lin Yang, Yu-Zhen Janice Chen, Stephen Pasteris, Mohammad Hajiesmaili, John C.S. Lui, and Don Towsley. 2021. Cooperative stochastic bandits with asynchronous agents and constrained feedback. Advances in Neural Information Processing Systems, Vol. 34 (2021), 8885--8897.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_61_1","volume-title":"Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs. arXiv preprint arXiv:2308.04314","author":"Yang Lin","year":"2023","unstructured":"Lin Yang, Xuchuang Wang, Lijun Zhang, Mohammad Hajiesmaili, John C.S. Lui, and Don Towsley. 2023. Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs. arXiv preprint arXiv:2308.04314 (2023)."},{"key":"e_1_2_1_62_1","volume-title":"Locally differentially private (contextual) bandits learning. arXiv preprint arXiv:2006.00701","author":"Zheng Kai","year":"2020","unstructured":"Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, and Liwei Wang. 2020. Locally differentially private (contextual) bandits learning. arXiv preprint arXiv:2006.00701 (2020)."},{"key":"e_1_2_1_63_1","volume-title":"On Differentially Private Federated Linear Contextual Bandits. arXiv preprint arXiv:2302.13945","author":"Zhou Xingyu","year":"2023","unstructured":"Xingyu Zhou and Sayak Ray Chowdhury. 2023. On Differentially Private Federated Linear Contextual Bandits. arXiv preprint arXiv:2302.13945 (2023)."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3410220.3453919"},{"key":"e_1_2_1_65_1","first-page":"1","article-title":"Tsallis-inf: An optimal algorithm for stochastic and adversarial bandits","volume":"22","author":"Zimmert Julian","year":"2021","unstructured":"Julian Zimmert and Yevgeny Seldin. 2021. Tsallis-inf: An optimal algorithm for stochastic and adversarial bandits. Journal of Machine Learning Research, Vol. 22, 28 (2021), 1--49.","journal-title":"Journal of Machine Learning Research"}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711696","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T02:20:52Z","timestamp":1755915652000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,6]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3,6]]}},"alternative-id":["10.1145\/3711696"],"URL":"https:\/\/doi.org\/10.1145\/3711696","relation":{},"ISSN":["2476-1249"],"issn-type":[{"value":"2476-1249","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,6]]},"assertion":[{"value":"2025-03-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}