{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:21Z","timestamp":1742914341508,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031703775"},{"type":"electronic","value":"9783031703782"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70378-2_22","type":"book-chapter","created":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:02:05Z","timestamp":1725181325000},"page":"351-367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time Series Clustering for\u00a0Enhanced Dynamic Allocation in\u00a0A\/B Testing"],"prefix":"10.1007","author":[{"given":"Emmanuelle","family":"Claeys","sequence":"first","affiliation":[]},{"given":"Myriam","family":"Maumy-Bertrand","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Gan\u00e7arski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"22_CR1","unstructured":"Agarwal, A., Dud\u00edk, M., Kale, S., Langford, J., Schapire, R.E.: Contextual bandit learning with predictable rewards. ArXiv e-prints, February 2012"},{"issue":"2","key":"22_CR2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1023\/A:1013689704352","volume":"47","author":"P Auer","year":"2002","unstructured":"Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2), 235\u2013256 (2002)","journal-title":"Mach. Learn."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Avadhanula, V., Colini-Baldeschi, R., Leonardi, S., Sankararaman, K.A., Schrijvers, O.: Stochastic bandits for multi-platform budget optimization in online advertising. CoRR (2021)","DOI":"10.1145\/3442381.3450074"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Bastani, H., Bayati, M.: Online decision-making with high-dimensional covariates. SSRN Electron. J. (2015)","DOI":"10.2139\/ssrn.2661896"},{"key":"22_CR5","unstructured":"Bietti, A., Agarwal, A., Langford, J.: A contextual bandit bake-off. JLMR (2021)"},{"issue":"1","key":"22_CR6","first-page":"335","volume":"35","author":"E Claeys","year":"2021","unstructured":"Claeys, E., Gancarski, P., Maumy-Bertrand, M., Wassner, H.: Dynamic allocation optimization in A\/B-tests using classification-based preprocessing. IEEE TKDE 35(1), 335\u2013349 (2021)","journal-title":"IEEE TKDE"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Fabijan, A., Dmitriev, P., Arai, B., Drake, A., Kohlmeier, S., Kwong, A.: A\/B integrations: 7 lessons learned from enabling A\/B testing (2023)","DOI":"10.1109\/ICSE-SEIP58684.2023.00033"},{"key":"22_CR8","unstructured":"Gentile, C., Li, S., Kar, P., Karatzoglou, A., Zappella, G., Etrue, E.: On context-dependent clustering of bandits. In: Proceedings of the 34th International Conference on Machine Learning (2017)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651\u2013674 (2006)","DOI":"10.1198\/106186006X133933"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Kaluza, B., Mirchevska, V., Dovgan, E., Lustrek, M., Gams, M.: UCI machine learning repository, an agent-based approach to care in independent living (2010)","DOI":"10.1007\/978-3-642-16917-5_18"},{"key":"22_CR11","unstructured":"Kaufmann, E., Capp\u00e9, O., Garivier, A.: On the complexity of A\/B testing. ArXiv e-prints, May 2014"},{"issue":"1","key":"22_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/0196-8858(85)90002-8","volume":"6","author":"T Lai","year":"1985","unstructured":"Lai, T., Robbins, H.: Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6(1), 4\u201322 (1985)","journal-title":"Adv. Appl. Math."},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Lattimore, T., Szepesv\u00e1ri, C.: Bandit algorithms (2020)","DOI":"10.1017\/9781108571401"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation, pp. 661\u2013670 (2010)","DOI":"10.1145\/1772690.1772758"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Mahadik, K., Wu, Q., Li, S., Sabne, A.: Fast distributed bandits for online recommendation systems. In: Proceedings of the 34th ACM International Conference on Supercomputing (2020)","DOI":"10.1145\/3392717.3392748"},{"key":"22_CR16","unstructured":"Maillard, O.A., Mannor, S.: Latent Bandits, January 2014. Extended version of the paper accepted to ICML 2014"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","volume":"44","author":"F Petitjean","year":"2011","unstructured":"Petitjean, F., Ketterlin, A., Gancarski, P.: A global averaging method for DTW, with applications to clustering. Pattern Recogn. 44, 678\u2013693 (2011)","journal-title":"Pattern Recogn."},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Rotman, M., Wolf, L.: Energy regularized RNNS for solving non-stationary bandit problems (2023)","DOI":"10.1109\/ICASSP49357.2023.10095643"},{"key":"22_CR19","first-page":"53","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. JCAM 20, 53\u201365 (1987)","journal-title":"JCAM"},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/biomet\/25.3-4.285","volume":"25","author":"WR Thompson","year":"1933","unstructured":"Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 285\u2013294 (1933)","journal-title":"Biometrika"},{"key":"22_CR21","unstructured":"Xu, D., Yang, B.: On the advances and challenges of adaptive online testing. In: WSDM 2022 (2022)"},{"key":"22_CR22","unstructured":"Zhang, Z., Yang, J., Ji, X., Du, S.S.: Variance-aware confidence set: variance-dependent bound for linear bandits and horizon-free bound for linear mixture MDP. CoRR (2021)"},{"key":"22_CR23","unstructured":"Zhou, D., Li, L., Gu, Q.: Neural contextual bandits with upper confidence bound-based exploration. CoRR (2019)"},{"key":"22_CR24","unstructured":"Zhou, X., Ji, B.: On kernelized multi-armed bandits with constraints. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2022)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70378-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T09:06:30Z","timestamp":1725181590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70378-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703775","9783031703782"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70378-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}