{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:33:16Z","timestamp":1769502796114,"version":"3.49.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Onfido"},{"DOI":"10.13039\/501100011878","name":"Vlaamse regering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011878","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2021,9,1]]},"DOI":"10.1109\/tpami.2020.3026019","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T23:22:04Z","timestamp":1600816924000},"page":"3024-3036","source":"Crossref","is-referenced-by-count":8,"title":["Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation"],"prefix":"10.1109","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9667-3768","authenticated-orcid":false,"given":"Xingchen","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-181X","authenticated-orcid":false,"given":"Matthew B.","family":"Blaschko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2007.915707"},{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01037"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1090\/S0273-0979-00-00865-X"},{"key":"ref36","author":"wainwright","year":"0","journal-title":"High-dimensional statistics A non-asymptotic viewpoint"},{"key":"ref35","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"maas","year":"2013"},{"key":"ref34","article-title":"Fast and accurate deep network learning by exponential linear units (ELUs)","author":"clevert","year":"2016"},{"key":"ref10","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Proc 24th Int Conf Neural Inf Process Syst"},{"key":"ref11","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.1080.0314"},{"key":"ref13","first-page":"1809","article-title":"Entropy search for information-efficient global optimization","volume":"13","author":"hennig","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref14","first-page":"1778","article-title":"Bayesian optimization in high dimensions via random embeddings","author":"wang","year":"2013","journal-title":"Proc 23rd Int Joint Conf Artif Intell"},{"key":"ref15","first-page":"6765","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"li","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/b97848"},{"key":"ref17","article-title":"A kernel for hierarchical parameter spaces","author":"hutter","year":"2013"},{"key":"ref18","article-title":"Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces","author":"swersky","year":"2014"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965867"},{"key":"ref28","article-title":"On local optimizers of acquisition functions in Bayesian optimization","author":"kim","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref27","article-title":"GPyOpt: A Bayesian optimization framework in python","year":"2016"},{"key":"ref3","article-title":"A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning","author":"brochu","year":"2010","journal-title":"CoRR"},{"key":"ref6","article-title":"A tutorial on Bayesian optimization","author":"frazier","year":"0"},{"key":"ref29","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","author":"bergstra","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref8","first-page":"1655","article-title":"Bayesian optimization with tree-structured dependencies","author":"jenatton","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref7","first-page":"528","article-title":"Fast Bayesian optimization of machine learning hyperparameters on large datasets","author":"klein","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref2","first-page":"1015","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design","author":"srinivas","year":"2010"},{"key":"ref9","first-page":"1015","article-title":"Additive tree-structured covariance function for conditional parameter spaces in Bayesian optimization","author":"ma","year":"2020","journal-title":"Proc 23rd Int Conf Artif Intell Statist"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"ref20","first-page":"226","article-title":"Additive Gaussian processes","author":"duvenaud","year":"2011","journal-title":"Proc 24th Int Conf Neural Inf Process Syst"},{"key":"ref22","first-page":"1311","article-title":"Discovering and exploiting additive structure for Bayesian optimization","author":"gardner","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref21","first-page":"295","article-title":"High dimensional Bayesian optimisation and bandits via additive models","author":"kandasamy","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546921"},{"key":"ref23","first-page":"298","article-title":"High-dimensional Bayesian optimization via additive models with overlapping groups","author":"rolland","year":"2018","journal-title":"Proc 21st Int Conf Artif Intell Statist"},{"key":"ref26","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref25","first-page":"1","article-title":"No-regret Bayesian optimization with unknown hyperparameters","volume":"20","author":"berkenkamp","year":"2019","journal-title":"J Mach Learn Res"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9506969\/09204443.pdf?arnumber=9204443","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:49:23Z","timestamp":1652194163000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9204443\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,1]]},"references-count":38,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3026019","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,1]]}}}