{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T16:47:47Z","timestamp":1793206067123,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["104400,105874"],"award-info":[{"award-number":["104400,105874"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,7]]},"DOI":"10.1145\/3449726.3463164","type":"proceedings-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T14:49:57Z","timestamp":1625755797000},"page":"1860-1869","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["How Bayesian should Bayesian optimisation be?"],"prefix":"10.1145","author":[{"given":"George","family":"De Ath","sequence":"first","affiliation":[{"name":"University of Exeter, Exeter, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard M.","family":"Everson","sequence":"additional","affiliation":[{"name":"University of Exeter, Exeter, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan E.","family":"Fieldsend","sequence":"additional","affiliation":[{"name":"University of Exeter, Exeter, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Andrew Gordon Wilson, and Eytan Bakshy","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. Curran Associates, Inc., New York , 21524--21538. 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. Curran Associates, Inc., New York, 21524--21538."},{"key":"e_1_3_2_2_2_1","volume-title":"Learning and Intelligent Optimization (Lecture Notes in Computer Science)","author":"Benassi Romain","unstructured":"Romain Benassi , Julien Bect , and Emmanuel Vazquez . 2011. Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion . In Learning and Intelligent Optimization (Lecture Notes in Computer Science) . Springer , Berlin, Heidelberg , 176--190. Romain Benassi, Julien Bect, and Emmanuel Vazquez. 2011. Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion. In Learning and Intelligent Optimization (Lecture Notes in Computer Science). Springer, Berlin, Heidelberg, 176--190."},{"key":"e_1_3_2_2_3_1","volume-title":"Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science)","author":"Berk Julian","unstructured":"Julian Berk , Vu Nguyen , Sunil Gupta , Santu Rana , and Svetha Venkatesh . 2019. Exploration Enhanced Expected Improvement for Bayesian Optimization . In Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science) . Springer International Publishing , Cham, Switzerland , 621--637. Julian Berk, Vu Nguyen, Sunil Gupta, Santu Rana, and Svetha Venkatesh. 2019. Exploration Enhanced Expected Improvement for Bayesian Optimization. In Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science). Springer International Publishing, Cham, Switzerland, 621--637."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_2_2_5_1","unstructured":"Eric Brochu Vlad M Cora and Nando De Freitas. 2010. A tutorial on Bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599  Eric Brochu Vlad M Cora and Nando De Freitas. 2010. A tutorial on Bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/0916069"},{"key":"e_1_3_2_2_7_1","unstructured":"Zhehui Chen Simon Mak and C. F. Jeff Wu. 2021. A hierarchical expected improvement method for Bayesian optimization. arXiv:1911.07285  Zhehui Chen Simon Mak and C. F. Jeff Wu. 2021. A hierarchical expected improvement method for Bayesian optimization. arXiv:1911.07285"},{"key":"e_1_3_2_2_8_1","unstructured":"Andreas Damianou and Neil D. Lawrence. 2013. Deep Gaussian Processes. In Artificial Intelligence and Statistics. PMLR 207--215.  Andreas Damianou and Neil D. Lawrence. 2013. Deep Gaussian Processes. In Artificial Intelligence and Statistics. PMLR 207--215."},{"key":"e_1_3_2_2_9_1","article-title":"Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation","volume":"1","author":"Ath George De","year":"2021","unstructured":"George De Ath , Richard M. Everson , Alma A. M. Rahat , and Jonathan E. Fieldsend . 2021 . Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation . ACM Transactions on Evolutionary Learning and Optimization 1 , 1 (2021). George De Ath, Richard M. Everson, Alma A. M. Rahat, and Jonathan E. Fieldsend. 2021. Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation. ACM Transactions on Evolutionary Learning and Optimization 1, 1 (2021).","journal-title":"ACM Transactions on Evolutionary Learning and Optimization"},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery","author":"Ath George De","unstructured":"George De Ath , Jonathan E. Fieldsend , and Richard M. Everson . 2020. What Do You Mean? The Role of the Mean Function in Bayesian Optimisation . In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery , New York, 1623--1631. George De Ath, Jonathan E. Fieldsend, and Richard M. Everson. 2020. What Do You Mean? The Role of the Mean Function in Bayesian Optimisation. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, New York, 1623--1631."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/0370-2693(87)91197-X"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-014-0210-2"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5388-x"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Peter I Frazier. 2018. A tutorial on Bayesian optimization. arXiv:1807.02811  Peter I Frazier. 2018. A tutorial on Bayesian optimization. arXiv:1807.02811","DOI":"10.1287\/educ.2018.0188"},{"key":"e_1_3_2_2_16_1","volume-title":"Advances in Neural Information Processing Systems. Curran Associates","author":"Gardner Jacob","unstructured":"Jacob Gardner , Geoff Pleiss , Kilian Q Weinberger , David Bindel , and Andrew G Wilson . 2018. GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration . In Advances in Neural Information Processing Systems. Curran Associates , Inc., New York , 7576--7586. Jacob Gardner, Geoff Pleiss, Kilian Q Weinberger, David Bindel, and Andrew G Wilson. 2018. GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration. In Advances in Neural Information Processing Systems. Curran Associates, Inc., New York, 7576--7586."},{"key":"e_1_3_2_2_17_1","volume-title":"since","year":"2012","unstructured":"GPy. since 2012 . GPy: A Gaussian process framework in python. http:\/\/github.com\/SheffieldML\/GPy. GPy. since 2012. GPy: A Gaussian process framework in python. http:\/\/github.com\/SheffieldML\/GPy."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1993.10485354"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"e_1_3_2_2_20_1","first-page":"1809","article-title":"Entropy search for information-efficient global optimization","volume":"13","author":"Hennig Philipp","year":"2012","unstructured":"Philipp Hennig and Christian J. Schuler . 2012 . Entropy search for information-efficient global optimization . The Journal of Machine Learning Research 13 , 1 (2012), 1809 -- 1837 . Philipp Hennig and Christian J. Schuler. 2012. Entropy search for information-efficient global optimization. The Journal of Machine Learning Research 13, 1 (2012), 1809--1837.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_2_21_1","volume-title":"Advances in Neural Information Processing Systems","author":"Henr\u00e1ndez-Lobato Jos\u00e9 Miguel","unstructured":"Jos\u00e9 Miguel Henr\u00e1ndez-Lobato , Matthew W. Hoffman , and Zoubin Ghahramani . 2014. Predictive entropy search for efficient global optimization of black-box functions . In Advances in Neural Information Processing Systems . MIT Press , Cambridge, Massachusetts , 918--926. Jos\u00e9 Miguel Henr\u00e1ndez-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani. 2014. Predictive entropy search for efficient global optimization of black-box functions. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, Massachusetts, 918--926."},{"key":"e_1_3_2_2_22_1","first-page":"1593","article-title":"The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo","volume":"15","author":"Hoffman Matthew D.","year":"2014","unstructured":"Matthew D. Hoffman and Andrew Gelman . 2014 . The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo . Journal of Machine Learning Research 15 , 47 (2014), 1593 -- 1623 . Matthew D. Hoffman and Andrew Gelman. 2014. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 47 (2014), 1593--1623.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_23_1","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm Sture","year":"1979","unstructured":"Sture Holm . 1979 . A simple sequentially rejective multiple test procedure . Scandinavian Journal of Statistics 6 , 2 (1979), 65 -- 70 . Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6, 2 (1979), 65--70.","journal-title":"Scandinavian Journal of Statistics"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"e_1_3_2_2_26_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes . arXiv:1312.6114 Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. arXiv:1312.6114"},{"key":"e_1_3_2_2_28_1","first-page":"1","article-title":"Automatic Differentiation Variational Inference","volume":"18","author":"Kucukelbir Alp","year":"2017","unstructured":"Alp Kucukelbir , Dustin Tran , Rajesh Ranganath , Andrew Gelman , and David M. Blei . 2017 . Automatic Differentiation Variational Inference . Journal of Machine Learning Research 18 , 14 (2017), 1 -- 45 . Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. 2017. Automatic Differentiation Variational Inference. Journal of Machine Learning Research 18, 14 (2017), 1--45.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.3653121"},{"key":"e_1_3_2_2_30_1","volume-title":"Approximate Inference for Fully Bayesian Gaussian Process Regression. In Symposium on Advances in Approximate Bayesian Inference. PMLR, 1--12","author":"Lalchand Vidhi","year":"2020","unstructured":"Vidhi Lalchand and Carl Edward Rasmussen . 2020 . Approximate Inference for Fully Bayesian Gaussian Process Regression. In Symposium on Advances in Approximate Bayesian Inference. PMLR, 1--12 . Vidhi Lalchand and Carl Edward Rasmussen. 2020. Approximate Inference for Fully Bayesian Gaussian Process Regression. In Symposium on Advances in Approximate Bayesian Inference. PMLR, 1--12."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.5.720"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976699300016331"},{"key":"e_1_3_2_2_34_1","first-page":"239","article-title":"A comparison of three methods for selecting values of input variables in the analysis of output from a computer code","volume":"21","author":"McKay Michael D.","year":"1979","unstructured":"Michael D. McKay , Richard J. Beckman , and William J. Conover . 1979 . A comparison of three methods for selecting values of input variables in the analysis of output from a computer code . Technometrics 21 , 2 (1979), 239 -- 245 . Michael D. McKay, Richard J. Beckman, and William J. Conover. 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 2 (1979), 239--245.","journal-title":"Technometrics"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.1699114"},{"key":"e_1_3_2_2_37_1","first-page":"117","article-title":"The application of Bayesian methods for seeking the extremum","volume":"2","author":"Mo\u010dkus Jonas","year":"1978","unstructured":"Jonas Mo\u010dkus , Vytautas Tie\u0161is , and Antanas \u017dilinskas . 1978 . The application of Bayesian methods for seeking the extremum . Towards Global Optimization 2 , 1 (1978), 117 -- 129 . Jonas Mo\u010dkus, Vytautas Tie\u0161is, and Antanas \u017dilinskas. 1978. The application of Bayesian methods for seeking the extremum. Towards Global Optimization 2, 1 (1978), 117--129.","journal-title":"Towards Global Optimization"},{"key":"e_1_3_2_2_38_1","volume-title":"Advances in neural information processing systems","author":"Murray Iain","unstructured":"Iain Murray and Ryan P Adams . 2010. Slice sampling covariance hyperparameters of latent Gaussian models . In Advances in neural information processing systems , Vol. 23 . Curran Associates, Inc. , New York , 1723--1731. Iain Murray and Ryan P Adams. 2010. Slice sampling covariance hyperparameters of latent Gaussian models. In Advances in neural information processing systems, Vol. 23. Curran Associates, Inc., New York, 1723--1731."},{"key":"e_1_3_2_2_39_1","series-title":"Lecture Notes in Statistics","volume-title":"Bayesian Learning for Neural Networks","author":"Neal Radford M.","unstructured":"Radford M. Neal . 1996. Bayesian Learning for Neural Networks . Lecture Notes in Statistics , Vol. 118 . Springer New York , New York . Radford M. Neal. 1996. Bayesian Learning for Neural Networks. Lecture Notes in Statistics, Vol. 118. Springer New York, New York."},{"key":"e_1_3_2_2_41_1","volume-title":"Handbook of Markov Chain Monte Carlo, Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng (Eds.)","author":"Neal Radford M.","unstructured":"Radford M. Neal . 2011. MCMC Using Hamiltonian Dynamics . In Handbook of Markov Chain Monte Carlo, Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng (Eds.) . Chapman and Hall\/CRC , New York , 113--162. Radford M. Neal. 2011. MCMC Using Hamiltonian Dynamics. In Handbook of Markov Chain Monte Carlo, Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng (Eds.). Chapman and Hall\/CRC, New York, 113--162."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1978.tb01643.x"},{"key":"e_1_3_2_2_45_1","volume-title":"Williams","author":"Rasmussen Carl Edward","year":"2006","unstructured":"Carl Edward Rasmussen and Christopher K. I . Williams . 2006 . Gaussian processes for machine learning. MIT Press , Cambridge, Massachusetts. Carl Edward Rasmussen and Christopher K. I. Williams. 2006. Gaussian processes for machine learning. MIT Press, Cambridge, Massachusetts."},{"key":"e_1_3_2_2_46_1","volume-title":"Variational Inference with Normalizing Flows. In International Conference on Machine Learning. PMLR, 1530--1538","author":"Rezende Danilo","year":"2015","unstructured":"Danilo Rezende and Shakir Mohamed . 2015 . Variational Inference with Normalizing Flows. In International Conference on Machine Learning. PMLR, 1530--1538 . Danilo Rezende and Shakir Mohamed. 2015. Variational Inference with Normalizing Flows. In International Conference on Machine Learning. PMLR, 1530--1538."},{"key":"e_1_3_2_2_47_1","volume-title":"Robert and George Casella","author":"Christian","year":"2005","unstructured":"Christian P. Robert and George Casella . 2005 . Monte Carlo Statistical Methods (Springer Texts in Statistics). Springer-Verlag , New York. Christian P. Robert and George Casella. 2005. Monte Carlo Statistical Methods (Springer Texts in Statistics). Springer-Verlag, New York."},{"key":"e_1_3_2_2_48_1","volume-title":"Fast Information-theoretic Bayesian Optimisation. In International Conference on Machine Learning. PMLR, 4384--4392","author":"Ru Binxin","year":"2018","unstructured":"Binxin Ru , Michael A. Osborne , Mark Mcleod , and Diego Granziol . 2018 . Fast Information-theoretic Bayesian Optimisation. In International Conference on Machine Learning. PMLR, 4384--4392 . Binxin Ru, Michael A. Osborne, Mark Mcleod, and Diego Granziol. 2018. Fast Information-theoretic Bayesian Optimisation. In International Conference on Machine Learning. PMLR, 4384--4392."},{"key":"e_1_3_2_2_49_1","volume-title":"Deep Gaussian Processes with Importance-Weighted Variational Inference. In International Conference on Machine Learning. PMLR, 5589--5598","author":"Salimbeni Hugh","year":"2019","unstructured":"Hugh Salimbeni , Vincent Dutordoir , James Hensman , and Marc Deisenroth . 2019 . Deep Gaussian Processes with Importance-Weighted Variational Inference. In International Conference on Machine Learning. PMLR, 5589--5598 . Hugh Salimbeni, Vincent Dutordoir, James Hensman, and Marc Deisenroth. 2019. Deep Gaussian Processes with Importance-Weighted Variational Inference. In International Conference on Machine Learning. PMLR, 5589--5598."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.55"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1137\/100801275"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_2_53_1","volume-title":"Advances in Neural Information Processing Systems. Curran Associates","author":"Snoek Jasper","unstructured":"Jasper Snoek , Hugo Larochelle , and Ryan P Adams . 2012. Practical Bayesian Optimization of Machine Learning Algorithms . In Advances in Neural Information Processing Systems. Curran Associates , Inc., New York , 2951--2959. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In Advances in Neural Information Processing Systems. Curran Associates, Inc., New York, 2951--2959."},{"key":"e_1_3_2_2_54_1","volume-title":"Input Warping for Bayesian Optimization of Non-Stationary Functions. In International Conference on Machine Learning. PMLR, 1674--1682","author":"Snoek Jasper","year":"2014","unstructured":"Jasper Snoek , Kevin Swersky , Rich Zemel , and Ryan Adams . 2014 . Input Warping for Bayesian Optimization of Non-Stationary Functions. In International Conference on Machine Learning. PMLR, 1674--1682 . Jasper Snoek, Kevin Swersky, Rich Zemel, and Ryan Adams. 2014. Input Warping for Bayesian Optimization of Non-Stationary Functions. In International Conference on Machine Learning. PMLR, 1674--1682."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-004-6733-1"},{"key":"e_1_3_2_2_56_1","volume-title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In International Conference on Machine Learning. Omnipress","author":"Srinivas Niranjan","year":"2010","unstructured":"Niranjan Srinivas , Andreas Krause , Sham Kakade , and Matthias Seeger . 2010 . Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In International Conference on Machine Learning. Omnipress , Madison, 1015--1022. Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. 2010. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In International Conference on Machine Learning. Omnipress, Madison, 1015--1022."},{"key":"e_1_3_2_2_57_1","volume-title":"Interpolation of Spatial Data","author":"Stein Michael L.","unstructured":"Michael L. Stein . 1999. Interpolation of Spatial Data . Springer New York , New York . Michael L. Stein. 1999. Interpolation of Spatial Data. Springer New York, New York."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000001"},{"key":"e_1_3_2_2_59_1","volume-title":"Max-value Entropy Search for Efficient Bayesian Optimization. In International Conference on Machine Learning. PMLR, 3627--3635","author":"Wang Zi","year":"2017","unstructured":"Zi Wang and Stefanie Jegelka . 2017 . Max-value Entropy Search for Efficient Bayesian Optimization. In International Conference on Machine Learning. PMLR, 3627--3635 . Zi Wang and Stefanie Jegelka. 2017. Max-value Entropy Search for Efficient Bayesian Optimization. In International Conference on Machine Learning. PMLR, 3627--3635."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/74.3.640"}],"event":{"name":"GECCO '21: Genetic and Evolutionary Computation Conference","location":"Lille France","acronym":"GECCO '21","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449726.3463164","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3449726.3463164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:02Z","timestamp":1750191422000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449726.3463164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":53,"alternative-id":["10.1145\/3449726.3463164","10.1145\/3449726"],"URL":"https:\/\/doi.org\/10.1145\/3449726.3463164","relation":{},"subject":[],"published":{"date-parts":[[2021,7,7]]},"assertion":[{"value":"2021-07-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}