{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T19:29:46Z","timestamp":1746300586290,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031445040"},{"type":"electronic","value":"9783031445057"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44505-7_5","type":"book-chapter","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T18:03:41Z","timestamp":1698170621000},"page":"62-77","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Optimization for\u00a0Function Compositions with\u00a0Applications to\u00a0Dynamic Pricing"],"prefix":"10.1007","author":[{"given":"Kunal","family":"Jain","sequence":"first","affiliation":[]},{"given":"K. J.","family":"Prabuchandran","sequence":"additional","affiliation":[]},{"given":"Tejas","family":"Bodas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"5_CR1","unstructured":"Astudillo, R., Frazier, P.: Bayesian optimization of composite functions. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 354\u2013363. PMLR (2019)"},{"issue":"1","key":"5_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2559152","volume":"3","author":"M Babaioff","year":"2015","unstructured":"Babaioff, M., Dughmi, S., Kleinberg, R., Slivkins, A.: Dynamic pricing with limited supply. ACM Trans. Econ. Comput. 3(1), 1\u201326 (2015)","journal-title":"ACM Trans. Econ. Comput."},{"key":"5_CR3","unstructured":"Balandat, M., et al.: BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"issue":"144","key":"5_CR4","first-page":"1","volume":"17","author":"RF Barber","year":"2016","unstructured":"Barber, R.F., Sidky, E.Y.: MOCCA: mirrored convex\/concave optimization for nonconvex composite functions. J. Mach. Learn. Res. 17(144), 1\u201351 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"5_CR5","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1287\/mnsc.2013.1788","volume":"60","author":"AV den Boer","year":"2014","unstructured":"den Boer, A.V., Zwart, B.: Simultaneously learning and optimizing using controlled variance pricing. Manage. Sci. 60(3), 770\u2013783 (2014)","journal-title":"Manage. Sci."},{"issue":"4","key":"5_CR6","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1287\/opre.2015.1397","volume":"63","author":"AV den Boer","year":"2015","unstructured":"den Boer, A.V., Zwart, B.: Dynamic pricing and learning with finite inventories. Oper. Res. 63(4), 965\u2013978 (2015)","journal-title":"Oper. Res."},{"key":"5_CR7","unstructured":"Brochu, E., Cora, V.M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning (2010)"},{"issue":"4","key":"5_CR8","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1287\/opre.1120.1057","volume":"60","author":"J Broder","year":"2012","unstructured":"Broder, J., Rusmevichientong, P.: Dynamic pricing under a general parametric choice model. Oper. Res. 60(4), 965\u2013980 (2012)","journal-title":"Oper. Res."},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Candelieri, A.: A gentle introduction to Bayesian optimization. In: 2021 Winter Simulation Conference (WSC), pp. 1\u201316 (2021)","DOI":"10.1109\/WSC52266.2021.9715413"},{"issue":"4","key":"5_CR10","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1086\/296587","volume":"65","author":"YM Chen","year":"1992","unstructured":"Chen, Y.M., Jain, D.C.: Dynamic monopoly pricing under a Poisson-type uncertain demand. J. Bus. 65(4), 593\u2013614 (1992)","journal-title":"J. Bus."},{"issue":"6","key":"5_CR11","doi-asserted-by":"publisher","first-page":"1722","DOI":"10.1287\/opre.2017.1629","volume":"65","author":"WC Cheung","year":"2017","unstructured":"Cheung, W.C., Simchi-Levi, D., Wang, H.: Technical note\u2014dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6), 1722\u20131731 (2017)","journal-title":"Oper. Res."},{"issue":"11","key":"5_CR12","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1287\/mnsc.2016.2526","volume":"63","author":"D Crapis","year":"2017","unstructured":"Crapis, D., Ifrach, B., Maglaras, C., Scarsini, M.: Monopoly pricing in the presence of social learning. Manage. Sci. 63(11), 3586\u20133608 (2017)","journal-title":"Manage. Sci."},{"issue":"1","key":"5_CR13","first-page":"1","volume":"20","author":"AV den Boer","year":"2015","unstructured":"den Boer, A.V.: Dynamic pricing and learning: historical origins, current research, and new directions. Surv. Oper. Res. Manage. Sci. 20(1), 1\u201318 (2015)","journal-title":"Surv. Oper. Res. Manage. Sci."},{"issue":"1\u20132","key":"5_CR14","first-page":"503","volume":"178","author":"D Drusvyatskiy","year":"2018","unstructured":"Drusvyatskiy, D., Paquette, C.: Efficiency of minimizing compositions of convex functions and smooth maps. Math. Program. 178(1\u20132), 503\u2013558 (2018)","journal-title":"Math. Program."},{"issue":"7","key":"5_CR15","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/e21070651","volume":"21","author":"DF Elreedy","year":"2019","unstructured":"Elreedy, D.F., Atiya, A.I., Shaheen, S.: A novel active learning regression framework for balancing the exploration-exploitation trade-off. Entropy 21(7), 651 (2019)","journal-title":"Entropy"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Frazier, P.I.: A tutorial on Bayesian optimization (2018)","DOI":"10.1287\/educ.2018.0188"},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1287\/mnsc.1110.1426","volume":"58","author":"JM Harrison","year":"2012","unstructured":"Harrison, J.M., Keskin, N.B., Zeevi, A.: Bayesian dynamic pricing policies: learning and earning under a binary prior distribution. Manage. Sci. 58(3), 570\u2013586 (2012)","journal-title":"Manage. Sci."},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Injadat, M., Salo, F., Nassif, A.B., Essex, A., Shami, A.: Bayesian optimization with machine learning algorithms towards anomaly detection. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136 (2018)","DOI":"10.1109\/GLOCOM.2018.8647714"},{"issue":"4","key":"5_CR19","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1287\/opre.1100.0903","volume":"59","author":"CW Kuo","year":"2011","unstructured":"Kuo, C.W., Ahn, H.S., Aydin, G.: Dynamic pricing of limited inventories when customers negotiate. Oper. Res. 59(4), 882\u2013897 (2011)","journal-title":"Oper. Res."},{"key":"5_CR20","unstructured":"Maddox, W., Balandat, M., Wilson, A.G., Bakshy, E.: Bayesian optimization with high-dimensional outputs. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021)"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Phillips, R.L.: Pricing and Revenue Optimization. Stanford University Press (2021)","DOI":"10.1515\/9781503614260"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Pyzer-Knapp, E.O.: Bayesian optimization for accelerated drug discovery. IBM J. Res. Dev. 62(6), 2:1\u20132:7 (2018)","DOI":"10.1147\/JRD.2018.2881731"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press (2005)","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"5_CR24","unstructured":"Scotto Di Perrotolo, A.: A theoretical framework for bayesian optimization convergence. Master\u2019s thesis, KTH, Optimization and Systems Theory (2018)"},{"key":"5_CR25","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)"},{"issue":"5","key":"5_CR26","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1109\/TIT.2011.2182033","volume":"58","author":"N Srinivas","year":"2012","unstructured":"Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Information-theoretic regret bounds for gaussian process optimization in the bandit setting. IEEE Trans. Inf. Theory 58(5), 3250\u20133265 (2012)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"5_CR27","unstructured":"Surjanovic, S., Bingham, D.: Virtual library of simulation experiments: test functions and datasets. https:\/\/www.sfu.ca\/~ssurjano. Accessed 7 Feb 2023"},{"key":"5_CR28","unstructured":"Woodworth, B.E., Srebro, N.: Tight complexity bounds for optimizing composite objectives. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016)"},{"issue":"1","key":"5_CR29","first-page":"26","volume":"17","author":"J Wu","year":"2019","unstructured":"Wu, J., Chen, X.Y., Zhang, H., Xiong, L.D., Lei, H., Deng, S.H.: Hyperparameter optimization for machine learning models based on Bayesian optimization. J. Electron. Sci. Technol. 17(1), 26\u201340 (2019)","journal-title":"J. Electron. Sci. Technol."}],"container-title":["Lecture Notes in Computer Science","Learning and Intelligent Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44505-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T18:04:47Z","timestamp":1698170687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44505-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031445040","9783031445057"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44505-7_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LION","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Learning and Intelligent Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nice","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lion2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lion17.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"83","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}