{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:56:07Z","timestamp":1773161767593,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000180","name":"U.S. Department of Homeland Security","doi-asserted-by":"publisher","award":["17STQAC00001-03- 03"],"award-info":[{"award-number":["17STQAC00001-03- 03"]}],"id":[{"id":"10.13039\/100000180","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1947203, IIS-2002540"],"award-info":[{"award-number":["IIS-1947203, IIS-2002540"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467299","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"35-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Multi-facet Contextual Bandits"],"prefix":"10.1145","author":[{"given":"Yikun","family":"Ban","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingrui","family":"He","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Curtiss B.","family":"Cook","sequence":"additional","affiliation":[{"name":"Mayo Clinic Arizona, Phoenix, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Yasin Abbasi-Yadkori D\u00e1vid P\u00e1l and Csaba Szepesv\u00e1ri. 2011. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems. 2312--2320.  Yasin Abbasi-Yadkori D\u00e1vid P\u00e1l and Csaba Szepesv\u00e1ri. 2011. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems. 2312--2320."},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR, 127--135","author":"Agrawal Shipra","year":"2013","unstructured":"Shipra Agrawal and Navin Goyal . 2013 . Thompson sampling for contextual bandits with linear payoffs . In International Conference on Machine Learning. PMLR, 127--135 . Shipra Agrawal and Navin Goyal. 2013. Thompson sampling for contextual bandits with linear payoffs. In International Conference on Machine Learning. PMLR, 127--135."},{"key":"e_1_3_2_2_3_1","volume-title":"International Conference on Machine Learning. PMLR, 242--252","author":"Allen-Zhu Zeyuan","year":"2019","unstructured":"Zeyuan Allen-Zhu , Yuanzhi Li , and Zhao Song . 2019 . A convergence theory for deep learning via over-parameterization . In International Conference on Machine Learning. PMLR, 242--252 . Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 2019. A convergence theory for deep learning via over-parameterization. In International Conference on Machine Learning. PMLR, 242--252."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12637-1_47"},{"key":"e_1_3_2_2_5_1","unstructured":"Sanjeev Arora Simon S Du Wei Hu Zhiyuan Li Russ R Salakhutdinov and Ruosong Wang. 2019. On exact computation with an infinitely wide neural net. In Advances in Neural Information Processing Systems. 8141--8150.  Sanjeev Arora Simon S Du Wei Hu Zhiyuan Li Russ R Salakhutdinov and Ruosong Wang. 2019. On exact computation with an infinitely wide neural net. In Advances in Neural Information Processing Systems. 8141--8150."},{"key":"e_1_3_2_2_6_1","volume-title":"Conference on Learning Theory (COLT). 41--53","author":"Audibert Jean-Yves","year":"2010","unstructured":"Jean-Yves Audibert and S\u00e9bastien Bubeck . 2010 . Best arm identification in multi-armed bandits . In Conference on Learning Theory (COLT). 41--53 . Jean-Yves Audibert and S\u00e9bastien Bubeck. 2010. Best arm identification in multi-armed bandits. In Conference on Learning Theory (COLT). 41--53."},{"key":"e_1_3_2_2_7_1","volume-title":"2002 a. Finite-time analysis of the multiarmed bandit problem. Machine learning","author":"Auer Peter","year":"2002","unstructured":"Peter Auer , Nicolo Cesa-Bianchi , and Paul Fischer . 2002 a. Finite-time analysis of the multiarmed bandit problem. Machine learning , Vol. 47 , 2--3 ( 2002 ), 235--256. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002 a. Finite-time analysis of the multiarmed bandit problem. Machine learning, Vol. 47, 2--3 (2002), 235--256."},{"key":"e_1_3_2_2_8_1","series-title":"SIAM journal on computing","volume-title":"2002 b. The nonstochastic multiarmed bandit problem","author":"Auer Peter","year":"2002","unstructured":"Peter Auer , Nicolo Cesa-Bianchi , Yoav Freund , and Robert E Schapire . 2002 b. The nonstochastic multiarmed bandit problem . SIAM journal on computing , Vol. 32 , 1 ( 2002 ), 48--77. Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. 2002 b. The nonstochastic multiarmed bandit problem. SIAM journal on computing, Vol. 32, 1 (2002), 48--77."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2018.8503252"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403134"},{"key":"e_1_3_2_2_11_1","volume-title":"Local Clustering in Contextual Multi-Armed Bandits. arXiv preprint arXiv:2103.00063","author":"Ban Yikun","year":"2021","unstructured":"Yikun Ban and Jingrui He. 2021. Local Clustering in Contextual Multi-Armed Bandits. arXiv preprint arXiv:2103.00063 ( 2021 ). Yikun Ban and Jingrui He. 2021. Local Clustering in Contextual Multi-Armed Bandits. arXiv preprint arXiv:2103.00063 (2021)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2019.1902"},{"key":"e_1_3_2_2_13_1","article-title":"X-Armed Bandits","volume":"12","author":"Bubeck S\u00e9bastien","year":"2011","unstructured":"S\u00e9bastien Bubeck , R\u00e9mi Munos , Gilles Stoltz , and Csaba Szepesv\u00e1ri . 2011 . X-Armed Bandits . Journal of Machine Learning Research , Vol. 12 , 5 (2011). S\u00e9bastien Bubeck, R\u00e9mi Munos, Gilles Stoltz, and Csaba Szepesv\u00e1ri. 2011. X-Armed Bandits. Journal of Machine Learning Research, Vol. 12, 5 (2011).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2013.6761049"},{"key":"e_1_3_2_2_15_1","unstructured":"Yuan Cao and Quanquan Gu. 2019. Generalization bounds of stochastic gradient descent for wide and deep neural networks. In Advances in Neural Information Processing Systems. 10836--10846.  Yuan Cao and Quanquan Gu. 2019. Generalization bounds of stochastic gradient descent for wide and deep neural networks. In Advances in Neural Information Processing Systems. 10836--10846."},{"key":"e_1_3_2_2_16_1","volume-title":"International Conference on Machine Learning. PMLR, 151--159","author":"Chen Wei","year":"2013","unstructured":"Wei Chen , Yajun Wang , and Yang Yuan . 2013 . Combinatorial multi-armed bandit: General framework and applications . In International Conference on Machine Learning. PMLR, 151--159 . Wei Chen, Yajun Wang, and Yang Yuan. 2013. Combinatorial multi-armed bandit: General framework and applications. In International Conference on Machine Learning. PMLR, 151--159."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946695"},{"key":"e_1_3_2_2_18_1","unstructured":"Aniket Anand Deshmukh Urun Dogan and Clay Scott. 2017. Multi-task learning for contextual bandits. In Advances in neural information processing systems. 4848--4856.  Aniket Anand Deshmukh Urun Dogan and Clay Scott. 2017. Multi-task learning for contextual bandits. In Advances in neural information processing systems. 4848--4856."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013445"},{"key":"e_1_3_2_2_20_1","volume-title":"Machine Learning for Healthcare Conference. 67--82","author":"Durand Audrey","year":"2018","unstructured":"Audrey Durand , Charis Achilleos , Demetris Iacovides , Katerina Strati , Georgios D Mitsis , and Joelle Pineau . 2018 . Contextual bandits for adapting treatment in a mouse model of de novo carcinogenesis . In Machine Learning for Healthcare Conference. 67--82 . Audrey Durand, Charis Achilleos, Demetris Iacovides, Katerina Strati, Georgios D Mitsis, and Joelle Pineau. 2018. Contextual bandits for adapting treatment in a mouse model of de novo carcinogenesis. In Machine Learning for Healthcare Conference. 67--82."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412127"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305511"},{"key":"e_1_3_2_2_23_1","volume-title":"International Conference on Machine Learning. 757--765","author":"Gentile Claudio","year":"2014","unstructured":"Claudio Gentile , Shuai Li , and Giovanni Zappella . 2014 . Online clustering of bandits . In International Conference on Machine Learning. 757--765 . Claudio Gentile, Shuai Li, and Giovanni Zappella. 2014. Online clustering of bandits. In International Conference on Machine Learning. 757--765."},{"key":"e_1_3_2_2_24_1","unstructured":"Arthur Jacot Franck Gabriel and Cl\u00e9ment Hongler. 2018. Neural tangent kernel: Convergence and generalization in neural networks. In Advances in neural information processing systems. 8571--8580.  Arthur Jacot Franck Gabriel and Cl\u00e9ment Hongler. 2018. Neural tangent kernel: Convergence and generalization in neural networks. In Advances in neural information processing systems. 8571--8580."},{"key":"e_1_3_2_2_25_1","volume-title":"HDMI: High-order Deep Multiplex Infomax. arXiv preprint arXiv:2102.07810","author":"Jing Baoyu","year":"2021","unstructured":"Baoyu Jing , Chanyoung Park , and Hanghang Tong . 2021 . HDMI: High-order Deep Multiplex Infomax. arXiv preprint arXiv:2102.07810 (2021). Baoyu Jing, Chanyoung Park, and Hanghang Tong. 2021. HDMI: High-order Deep Multiplex Infomax. arXiv preprint arXiv:2102.07810 (2021)."},{"key":"e_1_3_2_2_26_1","unstructured":"John Langford and Tong Zhang. 2008. The epoch-greedy algorithm for multi-armed bandits with side information. In Advances in neural information processing systems. 817--824.  John Langford and Tong Zhang. 2008. The epoch-greedy algorithm for multi-armed bandits with side information. In Advances in neural information processing systems. 817--824."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772758"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911548"},{"key":"e_1_3_2_2_30_1","volume-title":"International conference on machine learning. PMLR, 1245--1253","author":"Li Shuai","year":"2016","unstructured":"Shuai Li , Baoxiang Wang , Shengyu Zhang , and Wei Chen . 2016 b. Contextual combinatorial cascading bandits . In International conference on machine learning. PMLR, 1245--1253 . Shuai Li, Baoxiang Wang, Shengyu Zhang, and Wei Chen. 2016b. Contextual combinatorial cascading bandits. In International conference on machine learning. PMLR, 1245--1253."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11946"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946895"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973440.53"},{"key":"e_1_3_2_2_34_1","volume-title":"Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127","author":"Riquelme Carlos","year":"2018","unstructured":"Carlos Riquelme , George Tucker , and Jasper Snoek . 2018. Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127 ( 2018 ). Carlos Riquelme, George Tucker, and Jasper Snoek. 2018. Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling. arXiv preprint arXiv:1802.09127 (2018)."},{"key":"e_1_3_2_2_35_1","volume-title":"Nonlinear dimensionality reduction by locally linear embedding. science","author":"Roweis Sam T","year":"2000","unstructured":"Sam T Roweis and Lawrence K Saul . 2000. Nonlinear dimensionality reduction by locally linear embedding. science , Vol. 290 , 5500 ( 2000 ), 2323--2326. Sam T Roweis and Lawrence K Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. science, Vol. 290, 5500 (2000), 2323--2326."},{"key":"e_1_3_2_2_36_1","volume-title":"Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995","author":"Srinivas Niranjan","year":"2009","unstructured":"Niranjan Srinivas , Andreas Krause , Sham M Kakade , and Matthias Seeger . 2009. Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995 ( 2009 ). Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger. 2009. Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995 (2009)."},{"key":"e_1_3_2_2_37_1","volume-title":"A survey of collaborative filtering techniques. Advances in artificial intelligence","author":"Su Xiaoyuan","year":"2009","unstructured":"Xiaoyuan Su and Taghi M Khoshgoftaar . 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence , Vol. 2009 ( 2009 ). Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence, Vol. 2009 (2009)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/25.3-4.285"},{"key":"e_1_3_2_2_39_1","volume-title":"Finite-time analysis of kernelised contextual bandits. arXiv preprint arXiv:1309.6869","author":"Valko Michal","year":"2013","unstructured":"Michal Valko , Nathaniel Korda , R\u00e9mi Munos , Ilias Flaounas , and Nelo Cristianini . 2013. Finite-time analysis of kernelised contextual bandits. arXiv preprint arXiv:1309.6869 ( 2013 ). Michal Valko, Nathaniel Korda, R\u00e9mi Munos, Ilias Flaounas, and Nelo Cristianini. 2013. Finite-time analysis of kernelised contextual bandits. arXiv preprint arXiv:1309.6869 (2013)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911528"},{"key":"e_1_3_2_2_41_1","volume-title":"International Conference on Machine Learning. PMLR, 10746--10756","author":"Yang Lin","year":"2020","unstructured":"Lin Yang and Mengdi Wang . 2020 . Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound . In International Conference on Machine Learning. PMLR, 10746--10756 . Lin Yang and Mengdi Wang. 2020. Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound. In International Conference on Machine Learning. PMLR, 10746--10756."},{"key":"e_1_3_2_2_42_1","volume-title":"Deep neural linear bandits: Overcoming catastrophic forgetting through likelihood matching. arXiv preprint arXiv:1901.08612","author":"Zahavy Tom","year":"2019","unstructured":"Tom Zahavy and Shie Mannor . 2019. Deep neural linear bandits: Overcoming catastrophic forgetting through likelihood matching. arXiv preprint arXiv:1901.08612 ( 2019 ). Tom Zahavy and Shie Mannor. 2019. Deep neural linear bandits: Overcoming catastrophic forgetting through likelihood matching. arXiv preprint arXiv:1901.08612 (2019)."},{"key":"e_1_3_2_2_43_1","volume-title":"Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751","author":"Zheng Lecheng","year":"2021","unstructured":"Lecheng Zheng , Yu Cheng , Hongxia Yang , Nan Cao , and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751 ( 2021 ). Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. 2021. Deep Co-Attention Network for Multi-View Subspace Learning. arXiv preprint arXiv:2102.07751 (2021)."},{"key":"e_1_3_2_2_44_1","volume-title":"K Selcc uk Candan, and Hasan Davulcu","author":"Zhou Dawei","year":"2015","unstructured":"Dawei Zhou , Jingrui He , K Selcc uk Candan, and Hasan Davulcu . 2015 . MUVIR : Multi-View Rare Category Detection.. In IJCAI. Citeseer , 4098--4104. Dawei Zhou, Jingrui He, K Selcc uk Candan, and Hasan Davulcu. 2015. MUVIR: Multi-View Rare Category Detection.. In IJCAI. Citeseer, 4098--4104."},{"key":"e_1_3_2_2_45_1","volume-title":"International Conference on Machine Learning. PMLR, 11492--11502","author":"Zhou Dongruo","year":"2020","unstructured":"Dongruo Zhou , Lihong Li , and Quanquan Gu . 2020 a. Neural contextual bandits with UCB-based exploration . In International Conference on Machine Learning. PMLR, 11492--11502 . Dongruo Zhou, Lihong Li, and Quanquan Gu. 2020 a. Neural contextual bandits with UCB-based exploration. In International Conference on Machine Learning. PMLR, 11492--11502."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380288"},{"key":"e_1_3_2_2_47_1","volume-title":"Evren Korpeoglu, Kannan Achan, and Jingrui He. 2020 b. GAN-based Recommendation with Positive-Unlabeled Sampling. arXiv preprint arXiv:2012.06901","author":"Zhou Yao","year":"2020","unstructured":"Yao Zhou , Jianpeng Xu , Jun Wu , Zeinab Taghavi Nasrabadi , Evren Korpeoglu, Kannan Achan, and Jingrui He. 2020 b. GAN-based Recommendation with Positive-Unlabeled Sampling. arXiv preprint arXiv:2012.06901 ( 2020 ). Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, and Jingrui He. 2020 b. GAN-based Recommendation with Positive-Unlabeled Sampling. arXiv preprint arXiv:2012.06901 (2020)."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467299","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467299","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467299","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:29Z","timestamp":1750191509000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467299"}},"subtitle":["A Neural Network Perspective"],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":47,"alternative-id":["10.1145\/3447548.3467299","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467299","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}