{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T15:05:18Z","timestamp":1769267118421,"version":"3.49.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"crossref","award":["FA9550-19-1-0283, and FA9550-22-1-0244"],"award-info":[{"award-number":["FA9550-19-1-0283, and FA9550-22-1-0244"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["DMS2053489"],"award-info":[{"award-number":["DMS2053489"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>In many real-world problems, we are faced with the problem of selecting the best among a finite number of alternatives, where the best alternative is determined based on context specific information. In this work, we study the contextual Ranking and Selection problem under a finite-alternative-finite-context setting, where we aim to find the best alternative for each context. We use a separate Gaussian process to model the reward for each alternative and derive the large deviations rate function for both the expected and worst-case contextual probability of correct selection. We propose the GP-C-OCBA sampling policy, which uses the Gaussian process posterior to iteratively allocate observations to maximize the rate function. We prove its consistency and show that it achieves the optimal convergence rate under the assumption of a non-informative prior. Numerical experiments show that our algorithm is highly competitive in terms of sampling efficiency, while having significantly smaller computational overhead.<\/jats:p>","DOI":"10.1145\/3633456","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T11:54:47Z","timestamp":1700481287000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5845-8506","authenticated-orcid":false,"given":"Sait","family":"Cakmak","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9863-273X","authenticated-orcid":false,"given":"Yuhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-6393","authenticated-orcid":false,"given":"Siyang","family":"Gao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5399-6508","authenticated-orcid":false,"given":"Enlu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"21524","volume-title":"Advances in Neural Information Processing Systems 33","author":"Balandat Maximilian","year":"2020","unstructured":"Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy. 2020. BoTorch: A framework for efficient Monte Carlo Bayesian optimization. In Advances in Neural Information Processing Systems 33. Curran Associates, Red Hook, NY, 21524\u201321538."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-1384-8_3"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1142\/7437"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008349927281"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1017\/apr.2019.9"},{"issue":"5","key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1287\/opre.1040.0187","article-title":"Selection procedures with frequentist expected opportunity cost bounds","volume":"53","author":"Chick Stephen E.","year":"2005","unstructured":"Stephen E. Chick and Yaozhong Wu. 2005. Selection procedures with frequentist expected opportunity cost bounds. Operat. Res. 53, 5 (2005), 867\u2013878.","journal-title":"Operat. Res."},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-5320-4","volume-title":"Large Deviations Techniques and Applications (2nd ed.)","author":"Dembo Amir","year":"1998","unstructured":"Amir Dembo and Ofer Zeitouni. 1998. Large Deviations Techniques and Applications (2nd ed.). Springer-Verlag, Berlin."},{"key":"e_1_3_3_9_2","article-title":"Technical note\u2014Knowledge gradient for selection with covariates: Consistency and computation","author":"Ding Liang","year":"2021","unstructured":"Liang Ding, L. Jeff Hong, Haihui Shen, and Xiaowei Zhang. 2021. Technical note\u2014Knowledge gradient for selection with covariates: Consistency and computation. Naval Res. Log. 69, 3 (2021), 496\u2013507.","journal-title":"Naval Res. Log."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","unstructured":"Jianzhong Du Siyang Gao and Chun-Hung Chen. 2022. Rate-Optimal Contextual Ranking and Selection. Retrieved from https:\/\/arxiv.org\/abs\/2206.12640. DOI:10.48550\/ARXIV.2206.12640","DOI":"10.48550\/ARXIV.2206.12640"},{"key":"e_1_3_3_11_2","first-page":"22032","volume-title":"Advances in Neural Information Processing Systems 33","author":"Feng Qing","year":"2020","unstructured":"Qing Feng, Ben Letham, Hongzi Mao, and Eytan Bakshy. 2020. High-dimensional contextual policy search with unknown context rewards using Bayesian optimization. In Advances in Neural Information Processing Systems 33. Curran Associates, Red Hook, NY, 22032\u201322044."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1080.0314"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2015.06.005"},{"key":"e_1_3_3_14_2","volume-title":"Proceedings of the 15th International Conference on Automation Science and Engineering","author":"Gao Siyang","year":"2019","unstructured":"Siyang Gao, Jianzhong Du, and Chun-Hung Chen. 2019. Selecting the optimal system design under covariates. In Proceedings of the 15th International Conference on Automation Science and Engineering."},{"key":"e_1_3_3_15_2","first-page":"3952","volume-title":"Proceedings of the Winter Simulation Conference","author":"Gao Siyang","year":"2014","unstructured":"Siyang Gao and Leyuan Shi. 2014. An optimal opportunity cost selection procedure for a fixed number of designs. In Proceedings of the Winter Simulation Conference. IEEE, 3952\u20133958."},{"key":"e_1_3_3_16_2","first-page":"577","volume-title":"Proceedings of the Winter Simulation Conference","author":"Glynn Peter","year":"2004","unstructured":"Peter Glynn and Sandeep Juneja. 2004. A large deviations perspective on ordinal optimization. In Proceedings of the Winter Simulation Conference. IEEE, Piscataway, NJ, 577\u2013585."},{"key":"e_1_3_3_17_2","first-page":"1","article-title":"Real-time digital twin-based optimization with predictive simulation learning","author":"Goodwin Travis","year":"2022","unstructured":"Travis Goodwin, Jie Xu, Nurcin Celik, and Chun-Hung Chen. 2022. Real-time digital twin-based optimization with predictive simulation learning. J. Simul. (2022), 1\u201318.","journal-title":"J. Simul."},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177731721"},{"key":"e_1_3_3_19_2","unstructured":"Cheng Guo and Felix Berkhahn. 2016. Entity embeddings of categorical variables. Retrieved from https:\/\/arxiv:1604.06737"},{"key":"e_1_3_3_20_2","volume-title":"Proceedings of the 15th International Conference on Automation Science and Engineering","author":"Jin Xiao","year":"2019","unstructured":"Xiao Jin, Haobin Li, and Loo Hay Lee. 2019. Optimal budget allocation in simulation analytics*. In Proceedings of the 15th International Conference on Automation Science and Engineering."},{"key":"e_1_3_3_21_2","first-page":"162","volume-title":"Proceedings of the Winter Simulation Conference","author":"Kim Seong-Hee","year":"2007","unstructured":"Seong-Hee Kim and Barry L. Nelson. 2007. Recent advances in ranking and selection. In Proceedings of the Winter Simulation Conference. IEEE, Piscataway, NJ, 162\u2013172."},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.5555\/3466184.3466419"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.5555\/3522802.3523026"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719512"},{"key":"e_1_3_3_25_2","volume-title":"Proceedings of the 6th ACM Conference on Recommender Systems","author":"Nunes Maria Augusta S. N.","year":"2012","unstructured":"Maria Augusta S. N. Nunes and Rong Hu. 2012. Personality-based recommender systems: An overview. In Proceedings of the 6th ACM Conference on Recommender Systems."},{"key":"e_1_3_3_26_2","first-page":"2161","volume-title":"Proceedings of the Winter Simulation Conference","author":"Pearce Michael","year":"2017","unstructured":"Michael Pearce and Juergen Branke. 2017. Efficient expected improvement estimation for continuous multiple ranking and selection. In Proceedings of the Winter Simulation Conference. IEEE, Piscataway, NJ, 2161\u20132172."},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2018.03.017"},{"key":"e_1_3_3_28_2","unstructured":"Michael Pearce Janis Klaise and Matthew Groves. 2020. Practical Bayesian optimization of objectives with conditioning variables. Retrieved from https:\/\/arxiv:2002.09996"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0217595919400116"},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","DOI":"10.1287\/ijoc.2020.1009","article-title":"Ranking and selection with covariates for personalized decision making","author":"Shen Haihui","year":"2021","unstructured":"Haihui Shen, L. Jeff Hong, and Xiaowei Zhang. 2021. Ranking and selection with covariates for personalized decision making. INFORMS J. Comput. 33, 4 (2021), 1500\u20131519.","journal-title":"INFORMS J. Comput."},{"key":"e_1_3_3_31_2","volume-title":"Virtual Library of Simulation Experiments","author":"Surjanovic S.","year":"2013","unstructured":"S. Surjanovic and D. Bingham. 2013. Hartmann 3-dimensional function. In Virtual Library of Simulation Experiments. Retrieved from https:\/\/www.sfu.ca\/ssurjano\/hart3.html"}],"container-title":["ACM Transactions on Modeling and Computer Simulation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633456","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3633456","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:54:01Z","timestamp":1750287241000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3633456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4,30]]}},"alternative-id":["10.1145\/3633456"],"URL":"https:\/\/doi.org\/10.1145\/3633456","relation":{},"ISSN":["1049-3301","1558-1195"],"issn-type":[{"value":"1049-3301","type":"print"},{"value":"1558-1195","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"2022-01-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}