{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T12:52:59Z","timestamp":1776689579118,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,4]]},"DOI":"10.1145\/3632410.3632418","type":"proceedings-article","created":{"date-parts":[[2024,1,3]],"date-time":"2024-01-03T18:15:16Z","timestamp":1704305716000},"page":"173-181","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Practical First-Order Bayesian Optimization Algorithms"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0776-9616","authenticated-orcid":false,"given":"Utkarsh","family":"Prakash","sequence":"first","affiliation":[{"name":"IIT Dharwad, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2728-6320","authenticated-orcid":false,"given":"Aryan","family":"Chollera","sequence":"additional","affiliation":[{"name":"IIT Dharwad, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3040-5493","authenticated-orcid":false,"given":"Kushagra","family":"Khatwani","sequence":"additional","affiliation":[{"name":"IIT Dharwad, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6585-390X","authenticated-orcid":false,"given":"Prabuchandran","family":"K. J.","sequence":"additional","affiliation":[{"name":"IIT Dharwad, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9313-5662","authenticated-orcid":false,"given":"Tejas","family":"Bodas","sequence":"additional","affiliation":[{"name":"IIIT Hyderabad, India"}]}],"member":"320","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning. Journal of Machine Learning Research","author":"Aaron\u00a0Wilson Alan\u00a0Fern","year":"2014","unstructured":"Alan\u00a0Fern Aaron\u00a0Wilson and Prasad Tadepalli. 2014. Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning. Journal of Machine Learning Research (2014)."},{"key":"e_1_3_2_1_2_1","volume-title":"BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. Advances in neural information processing systems 33","author":"Balandat Maximilian","year":"2020","unstructured":"Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew\u00a0G Wilson, and Eytan Bakshy. 2020. BoTorch: a framework for efficient Monte-Carlo Bayesian optimization. Advances in neural information processing systems 33 (2020), 21524\u201321538."},{"key":"e_1_3_2_1_3_1","volume-title":"NIPS BayesOpt 2017 workshop.","author":"Beland J","year":"2017","unstructured":"Justin\u00a0J Beland and Prasanth\u00a0B Nair. 2017. Bayesian optimization under uncertainty. In NIPS BayesOpt 2017 workshop."},{"key":"e_1_3_2_1_4_1","volume-title":"International conference on machine learning. PMLR, 115\u2013123","author":"Bergstra James","year":"2013","unstructured":"James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning. PMLR, 115\u2013123."},{"key":"e_1_3_2_1_5_1","unstructured":"Ilija Bogunovic Jonathan Scarlett and Volkan Cevher. 2016. Time-varying Gaussian process bandit optimization. In Artificial Intelligence and Statistics. PMLR 314\u2013323."},{"key":"e_1_3_2_1_6_1","first-page":"22234","article-title":"EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization","volume":"34","author":"Bohdal Ondrej","year":"2021","unstructured":"Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales. 2021. EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization. Advances in Neural Information Processing Systems 34 (2021), 22234\u201322246.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_7_1","volume-title":"A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599","author":"Brochu Eric","year":"2010","unstructured":"Eric Brochu, Vlad\u00a0M Cora, and Nando De\u00a0Freitas. 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599 (2010)."},{"key":"e_1_3_2_1_8_1","volume-title":"Openai gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20604"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Machine Learning. PMLR, 1437\u20131446","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and efficient hyperparameter optimization at scale. In International Conference on Machine Learning. PMLR, 1437\u20131446."},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Machine Learning. PMLR, 1165\u20131173","author":"Franceschi Luca","year":"2017","unstructured":"Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil. 2017. Forward and reverse gradient-based hyperparameter optimization. In International Conference on Machine Learning. PMLR, 1165\u20131173."},{"key":"e_1_3_2_1_12_1","volume-title":"A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811","author":"Frazier I","year":"2018","unstructured":"Peter\u00a0I Frazier. 2018. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 (2018)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16933"},{"key":"e_1_3_2_1_15_1","volume-title":"Predictive entropy search for efficient global optimization of black-box functions. Advances in neural information processing systems 27","author":"Hern\u00e1ndez-Lobato Jos\u00e9\u00a0Miguel","year":"2014","unstructured":"Jos\u00e9\u00a0Miguel Hern\u00e1ndez-Lobato, Matthew\u00a0W Hoffman, and Zoubin Ghahramani. 2014. Predictive entropy search for efficient global optimization of black-box functions. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11788"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-005-2454-3"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44505-7_5"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Harold\u00a0J Kushner. 1964. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. (1964).","DOI":"10.1115\/1.3653121"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/35.41400"},{"key":"e_1_3_2_1_22_1","unstructured":"Yann LeCun and Corinna Cortes. 2010. MNIST handwritten digit database. http:\/\/yann.lecun.com\/exdb\/mnist\/. (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"e_1_3_2_1_23_1","volume-title":"International conference on machine learning. PMLR, 2113\u20132122","author":"Maclaurin Dougal","year":"2015","unstructured":"Dougal Maclaurin, David Duvenaud, and Ryan Adams. 2015. Gradient-based hyperparameter optimization through reversible learning. In International conference on machine learning. PMLR, 2113\u20132122."},{"key":"e_1_3_2_1_24_1","volume-title":"Toward global optimization","author":"Mockus Jonas","year":"1978","unstructured":"Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas. 1978. Toward global optimization, volume 2, chapter bayesian methods for seeking the extremum. (1978)."},{"key":"e_1_3_2_1_25_1","first-page":"20708","article-title":"Local policy search with Bayesian optimization","volume":"34","author":"M\u00fcller Sarah","year":"2021","unstructured":"Sarah M\u00fcller, Alexander von Rohr, and Sebastian Trimpe. 2021. Local policy search with Bayesian optimization. Advances in Neural Information Processing Systems 34 (2021), 20708\u201320720.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","volume-title":"Efficient high dimensional bayesian optimization with additivity and quadrature fourier features. Advances in Neural Information Processing Systems 31","author":"Mutny Mojmir","year":"2018","unstructured":"Mojmir Mutny and Andreas Krause. 2018. Efficient high dimensional bayesian optimization with additivity and quadrature fourier features. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5971"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2012.707580"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01896-w"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2018.2881731"},{"key":"e_1_3_2_1_31_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/100801275"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2836\u20132844","author":"Shekhar Shubhanshu","year":"2021","unstructured":"Shubhanshu Shekhar and Tara Javidi. 2021. Significance of gradient information in bayesian optimization. In International Conference on Artificial Intelligence and Statistics. PMLR, 2836\u20132844."},{"key":"e_1_3_2_1_35_1","volume-title":"Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek, Hugo Larochelle, and Ryan\u00a0P Adams. 2012. Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Srinivas N","year":"2010","unstructured":"N Srinivas. 2010. Gaussian process optimization in the bandit setting: No regret and experimental design. In Proceedings of the International Conference on Machine Learning, 2010."},{"key":"e_1_3_2_1_37_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\u00a0M 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_1_38_1","volume-title":"Virtual Library of Simulation Experiments: Test Functions and Datasets. Retrieved","author":"Surjanovic S.","year":"2022","unstructured":"S. Surjanovic and D. Bingham. 2022. Virtual Library of Simulation Experiments: Test Functions and Datasets. Retrieved August 15, 2022, from http:\/\/www.sfu.ca\/\u00a0ssurjano (2022)."},{"key":"e_1_3_2_1_39_1","unstructured":"Richard\u00a0S Sutton David\u00a0A McAllester Satinder\u00a0P Singh and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems. 1057\u20131063."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11830"},{"key":"e_1_3_2_1_41_1","unstructured":"Ziyu Wang Masrour Zoghi Frank Hutter David Matheson Nando De\u00a0Freitas 2013. Bayesian Optimization in High Dimensions via Random Embeddings.. In IJCAI. Citeseer 1778\u20131784."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17233"},{"key":"e_1_3_2_1_43_1","volume-title":"Gaussian processes for machine learning. Vol.\u00a02","author":"Williams K","unstructured":"Christopher\u00a0K Williams and Carl\u00a0Edward Rasmussen. 2006. Gaussian processes for machine learning. Vol.\u00a02. MIT press Cambridge, MA."},{"key":"e_1_3_2_1_44_1","volume-title":"Bayesian optimization with gradients. Advances in neural information processing systems 30","author":"Wu Jian","year":"2017","unstructured":"Jian Wu, Matthias Poloczek, Andrew\u00a0G Wilson, and Peter Frazier. 2017. Bayesian optimization with gradients. Advances in neural information processing systems 30 (2017)."}],"event":{"name":"CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)","location":"Bangalore India","acronym":"CODS-COMAD 2024"},"container-title":["Proceedings of the 7th Joint International Conference on Data Science &amp; Management of Data (11th ACM IKDD CODS and 29th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632418","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632410.3632418","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T13:34:15Z","timestamp":1755869655000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632410.3632418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":44,"alternative-id":["10.1145\/3632410.3632418","10.1145\/3632410"],"URL":"https:\/\/doi.org\/10.1145\/3632410.3632418","relation":{},"subject":[],"published":{"date-parts":[[2024,1,4]]},"assertion":[{"value":"2024-01-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}