{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:06:16Z","timestamp":1757617576458,"version":"3.44.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s10707-025-00541-3","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T20:04:02Z","timestamp":1741291442000},"page":"581-602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fair routing in MoE for distributed spatial data: a combinatorial multi-armed bandit solution"],"prefix":"10.1007","volume":"29","author":[{"given":"Yan","family":"Fu","sequence":"first","affiliation":[]},{"given":"Shasha","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yucong","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zichen","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"541_CR1","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"541_CR2","unstructured":"ChatGPT (2024) Optimizing Language Models for Dialogue. https:\/\/openai.com\/index\/chatgpt\/"},{"key":"541_CR3","unstructured":"Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q, Hinton G, Dean J (2017) Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv:1701.06538"},{"issue":"120","key":"541_CR4","first-page":"1","volume":"23","author":"W Fedus","year":"2022","unstructured":"Fedus W, Zoph B, Shazeer N (2022) Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. J Mach Learn Res 23(120):1\u201339","journal-title":"J Mach Learn Res"},{"key":"541_CR5","first-page":"15625","volume":"33","author":"MA Raihan","year":"2020","unstructured":"Raihan MA, Aamodt T (2020) Sparse weight activation training. Adv Neural Inf Process Syst 33:15625\u201315638","journal-title":"Adv Neural Inf Process Syst"},{"key":"541_CR6","unstructured":"Du N, Huang Y, Dai AM, Tong S, Lepikhin D, Xu Y, Krikun M, Zhou Y, Yu AW, Firat O, et al. (2022) Glam: Efficient scaling of language models with mixture-of-experts. In: International Conference on Machine Learning, pp 5547\u20135569 . PMLR"},{"key":"541_CR7","unstructured":"Rajbhandari S, Li C, Yao Z, Zhang M, Aminabadi RY, Awan AA, Rasley J, He Y (2022) Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale. In: International Conference on Machine Learning, pp 18332\u201318346. PMLR"},{"key":"541_CR8","unstructured":"Li J, Jiang Y, Zhu Y, Wang C, Xu H (2023) Accelerating distributed $$\\{$$MoE$$\\}$$ training and inference with lina. In: 2023 USENIX Annual Technical Conference (USENIX ATC 23), pp 945\u2013959"},{"key":"541_CR9","unstructured":"Lewis M, Bhosale S, Dettmers T, Goyal N, Zettlemoyer L (2021) Base layers: Simplifying training of large, sparse models. In: International Conference on Machine Learning, pp 6265\u20136274. PMLR"},{"key":"541_CR10","doi-asserted-by":"crossref","unstructured":"Wang W, Lai Z, Li S, Liu W, Ge K, Liu Y, Shen A, Li D (2023) Prophet: Fine-grained load balancing for parallel training of large-scale moe models. In: 2023 IEEE International Conference on Cluster Computing (CLUSTER), pp 82\u201394. IEEE","DOI":"10.1109\/CLUSTER52292.2023.00015"},{"key":"541_CR11","unstructured":"Lepikhin D, Lee H, Xu Y, Chen D, Firat O, Huang Y, Krikun M, Shazeer N, Chen Z (2021) Gshard: Scaling giant models with conditional computation and automatic sharding. In: International Conference on Learning Representations"},{"key":"541_CR12","first-page":"7103","volume":"35","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Lei T, Liu H, Du N, Huang Y, Zhao V, Dai AM, Le QV, Laudon J et al (2022) Mixture-of-experts with expert choice routing. Adv Neural Inf Process Syst 35:7103\u20137114","journal-title":"Adv Neural Inf Process Syst"},{"key":"541_CR13","unstructured":"Chen W, Wang Y, Yuan Y (2013) Combinatorial multi-armed bandit: General framework and applications. In: International Conference on Machine Learning, pp 151\u2013159. PMLR"},{"key":"541_CR14","doi-asserted-by":"crossref","unstructured":"Bouneffouf D, Rish I, Aggarwal C (2020) Survey on applications of multi-armed and contextual bandits. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp 1\u20138. IEEE","DOI":"10.1109\/CEC48606.2020.9185782"},{"key":"541_CR15","doi-asserted-by":"crossref","unstructured":"Ontan\u00f3n S (2013) The combinatorial multi-armed bandit problem and its application to real-time strategy games. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol 9, pp 58\u201364","DOI":"10.1609\/aiide.v9i1.12681"},{"key":"541_CR16","unstructured":"Chen Y, Cuellar A, Luo H, Modi J, Nemlekar H, Nikolaidis S (2020) Fair contextual multi-armed bandits: Theory and experiments. In: Conference on Uncertainty in Artificial Intelligence, pp 181\u2013190. PMLR"},{"issue":"1","key":"541_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s41019-022-00177-5","volume":"7","author":"H Zhu","year":"2022","unstructured":"Zhu H, Li W, Liu W, Yin J, Xu J (2022) Top k optimal sequenced route query with poi preferences. Data Sci Eng 7(1):3\u201315","journal-title":"Data Sci Eng"},{"key":"541_CR18","doi-asserted-by":"crossref","unstructured":"Liu H, Jin C, Yang B, Zhou A (2018) Finding top-k optimal sequenced routes. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp 569\u2013580. IEEE","DOI":"10.1109\/ICDE.2018.00058"},{"issue":"1","key":"541_CR19","doi-asserted-by":"publisher","first-page":"7352","DOI":"10.1038\/s41598-022-10648-4","volume":"12","author":"T Xu","year":"2022","unstructured":"Xu T, Xu A, Mango J, Liu P, Ma X, Zhang L (2022) Efficient processing of top-k frequent spatial keyword queries. Sci Report 12(1):7352","journal-title":"Sci Report"},{"key":"541_CR20","doi-asserted-by":"crossref","unstructured":"Li J, Xiong X, Li L, He D, Zong C, Zhou X (2023) Finding top-k optimal routes with collective spatial keywords on road networks. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), pp 368\u2013380. IEEE","DOI":"10.1109\/ICDE55515.2023.00035"},{"key":"541_CR21","doi-asserted-by":"crossref","unstructured":"He J, Zhai J, Antunes T, Wang H, Luo F, Shi S, Li Q (2022) Fastermoe: modeling and optimizing training of large-scale dynamic pre-trained models. In: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pp 120\u2013134","DOI":"10.1145\/3503221.3508418"},{"key":"541_CR22","doi-asserted-by":"crossref","unstructured":"Singh S, Ruwase O, Awan AA, Rajbhandari S, He Y, Bhatele A (2023) A hybrid tensor-expert-data parallelism approach to optimize mixture-of-experts training. In: Proceedings of the 37th International Conference on Supercomputing, pp 203\u2013214","DOI":"10.1145\/3577193.3593704"},{"key":"541_CR23","doi-asserted-by":"crossref","unstructured":"Artetxe M, Bhosale S, Goyal N, Mihaylov T, Ott M, Shleifer S, Lin XV, Du J, Iyer S, Pasunuru R, et al. (2022) Efficient large scale language modeling with mixtures of experts. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp 11699\u201311732","DOI":"10.18653\/v1\/2022.emnlp-main.804"},{"key":"541_CR24","unstructured":"Chowdhury MNR, Zhang S, Wang M, Liu S, Chen P-Y (2023) Patch-level routing in mixture-of-experts is provably sample-efficient for convolutional neural networks. In: International Conference on Machine Learning, pp 6074\u20136114. PMLR"},{"key":"541_CR25","unstructured":"Hwang C, Cui W, Xiong Y, Yang Z, Liu Z, Hu H, Wang Z, Salas R, Jose J, Ram P, et al. (2023) Tutel: Adaptive mixture-of-experts at scale. Proceed Mach Learn Syst 5"},{"key":"541_CR26","doi-asserted-by":"crossref","unstructured":"Yu D, Shen L, Hao H, Gong W, Wu H, Bian J, Dai L, Xiong H (2024) Moesys: A distributed and efficient mixture-of-experts training and inference system for internet services. IEEE Trans Serv Comput","DOI":"10.1109\/TSC.2024.3399654"},{"key":"541_CR27","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1090\/S0002-9904-1952-09620-8","volume":"58","author":"H Robbins","year":"1952","unstructured":"Robbins H (1952) Some aspects of the sequential design of experiments. Bulletin of the American Math Soc 58:527\u2013535","journal-title":"Bulletin of the American Math Soc"},{"issue":"1","key":"541_CR28","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1214\/aoms\/1177703739","volume":"35","author":"E Paulson","year":"1964","unstructured":"Paulson E (1964) A sequential procedure for selecting the population with the largest mean from k normal populations. The Annal Math Stat 35(1):174\u2013180","journal-title":"The Annal Math Stat"},{"key":"541_CR29","unstructured":"Shi C, Shen C, Yang J (2021) Federated multi-armed bandits with personalization. In: International Conference on Artificial Intelligence and Statistics, pp 2917\u20132925. PMLR"},{"key":"541_CR30","doi-asserted-by":"crossref","unstructured":"Wang L, Wang C, Wang K, He X (2017) Biucb: A contextual bandit algorithm for cold-start and diversified recommendation. In: 2017 IEEE International Conference on Big Knowledge (ICBK), pp 248\u2013253","DOI":"10.1109\/ICBK.2017.49"},{"issue":"5","key":"541_CR31","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1016\/j.jcss.2011.12.028","volume":"78","author":"Y Yue","year":"2012","unstructured":"Yue Y, Broder J, Kleinberg R, Joachims T (2012) The k-armed dueling bandits problem. J Comput Syst Sci 78(5):1538\u20131556","journal-title":"J Comput Syst Sci"},{"key":"541_CR32","doi-asserted-by":"crossref","unstructured":"Gai Y, Krishnamachari B, Jain R (2010) Learning multiuser channel allocations in cognitive radio networks: A combinatorial multi-armed bandit formulation. In: 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN), pp 1\u20139","DOI":"10.1109\/DYSPAN.2010.5457857"},{"key":"541_CR33","unstructured":"Abe N, Long PM (1999) Associative reinforcement learning using linear probabilistic concepts. In: ICML, pp 3\u201311. Citeseer"},{"key":"541_CR34","unstructured":"Auer P (2002) Using confidence bounds for exploitation-exploration trade-offs. J Mach Learn Res 3(Nov):397\u2013422"},{"issue":"1","key":"541_CR35","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1287\/opre.2019.1902","volume":"68","author":"H Bastani","year":"2020","unstructured":"Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Operation Res 68(1):276\u2013294","journal-title":"Operation Res"},{"issue":"1","key":"541_CR36","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1287\/11-SSY032","volume":"3","author":"A Goldenshluger","year":"2013","unstructured":"Goldenshluger A, Zeevi A (2013) A linear response bandit problem. Stochast Syst 3(1):230\u2013261","journal-title":"Stochast Syst"},{"issue":"3","key":"541_CR37","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1287\/mnsc.2020.3605","volume":"67","author":"H Bastani","year":"2021","unstructured":"Bastani H, Bayati M, Khosravi K (2021) Mostly exploration-free algorithms for contextual bandits. Manage Sci 67(3):1329\u20131349","journal-title":"Manage Sci"},{"issue":"1","key":"541_CR38","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1214\/aos\/1015362186","volume":"30","author":"Y Yang","year":"2002","unstructured":"Yang Y, Zhu D (2002) Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates. The Annal Stat 30(1):100\u2013121","journal-title":"The Annal Stat"},{"key":"541_CR39","unstructured":"Gillen S, Jung C, Kearns M, Roth A (2018) Online learning with an unknown fairness metric. Neural Inf Process Syst"},{"key":"541_CR40","unstructured":"Joseph M, Kearns M, Morgenstern JH, Roth A (2016) Fairness in learning: Classic and contextual bandits. Adv Neural Inf Process Syst 29"},{"key":"541_CR41","unstructured":"Hossain S, Micha E, Shah N (2020) Fair algorithms for multi-agent multi-armed bandits. In: Neural Information Processing Systems"},{"key":"541_CR42","unstructured":"Kim MP, Reingold O, Rothblum GN (2018) Fairness through computationally-bounded awareness. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS\u201918, pp 4847\u20134857. Curran Associates Inc., Red Hook, NY, USA"},{"key":"541_CR43","unstructured":"Bistritz I, Baharav T, Leshem A, Bambos N (2020) My fair bandit: Distributed learning of max-min fairness with multi-player bandits. In: International Conference on Machine Learning, pp 930\u2013940. PMLR"},{"key":"541_CR44","doi-asserted-by":"crossref","unstructured":"Patil V, Ghalme G, Nair V, Narahari Y (2020) Achieving fairness in the stochastic multi-armed bandit problem. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 5379\u20135386","DOI":"10.1609\/aaai.v34i04.5986"},{"issue":"3","key":"541_CR45","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1109\/TNSE.2019.2954310","volume":"7","author":"F Li","year":"2019","unstructured":"Li F, Liu J, Ji B (2019) Combinatorial sleeping bandits with fairness constraints. IEEE Trans Netw Sci Eng 7(3):1799\u20131813","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"541_CR46","doi-asserted-by":"crossref","unstructured":"Li F, Liu J, Ji B (2019) Combinatorial sleeping bandits with fairness constraints. IEEE INFOCOM 2019 - IEEE Conf Comput Commun, 1702\u20131710","DOI":"10.1109\/INFOCOM.2019.8737461"},{"key":"541_CR47","doi-asserted-by":"crossref","unstructured":"Li S, Cui Y, Zhao Y, Yang W, Zhang R, Zhou X (2023) St-moe: Spatio-temporal mixture-of-experts for debiasing in traffic prediction. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp 1208\u20131217","DOI":"10.1145\/3583780.3615068"},{"key":"541_CR48","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 753\u2013763","DOI":"10.1145\/3394486.3403118"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-025-00541-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-025-00541-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-025-00541-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:53:51Z","timestamp":1757141631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-025-00541-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":48,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["541"],"URL":"https:\/\/doi.org\/10.1007\/s10707-025-00541-3","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"type":"print","value":"1384-6175"},{"type":"electronic","value":"1573-7624"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"9 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"We have considered the ethical implications and have none to report.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}