{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T05:28:41Z","timestamp":1744954121148,"version":"3.40.2"},"reference-count":81,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1553281","1956313","2119103","2215573"],"award-info":[{"award-number":["1553281","1956313","2119103","2215573"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tase.2024.3475951","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T18:09:56Z","timestamp":1729102196000},"page":"7979-7991","source":"Crossref","is-referenced-by-count":1,"title":["Rare Event Detection by Acquisition-Guided Sampling"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2862-9508","authenticated-orcid":false,"given":"Huiling","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4347-2476","authenticated-orcid":false,"given":"Xiaoning","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Department of Computer Science and Engineering, Texas A&#x0026;M University, College Station, TX, USA"}]},{"given":"Jianhua Z.","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3548-4589","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1146909.1146930"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CICC.2015.7338409"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2009.2020721"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2021.102174"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/0266-8920(89)90024-6"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1061\/JMCEA3.0002512"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2011.01.002"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2020.102011"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2990401"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2022.3213827"},{"key":"ref11","volume":"80","author":"Adler","year":"2007","journal-title":"Random Fields and Geometry"},{"article-title":"Fast uncertainty reduction strategies relying on Gaussian process models","year":"2013","author":"Chevalier","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2013.860918"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2019.1693427"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1137\/141000749"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240835"},{"key":"ref17","first-page":"1","article-title":"Enabling high-dimensional Bayesian optimization for efficient failure detection of analog and mixed-signal circuits","volume-title":"Proc. 56th ACM\/IEEE Design Autom. Conf. (DAC)","author":"Hu"},{"key":"ref18","first-page":"2951","article-title":"Practical Bayesian optimization of machine learning algorithms","volume-title":"Proc. 25th Int. Conf. Neural Inf. Process. Syst.","volume":"2","author":"Snoek"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1115\/1.3653121"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-38527-2_55"},{"key":"ref22","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design","author":"Srinivas","year":"2009","journal-title":"arXiv:0912.3995"},{"issue":"6","key":"ref23","first-page":"1809","article-title":"Entropy search for information-efficient global optimization","volume":"13","author":"Hennig","year":"2012","journal-title":"J. Mach. Learn. Res."},{"issue":"50","key":"ref24","first-page":"1","article-title":"No-regret Bayesian optimization with unknown hyperparameters","volume":"20","author":"Berkenkamp","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2023.2275166"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2020.1826467"},{"key":"ref27","first-page":"3627","article-title":"Max-value entropy search for efficient Bayesian optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref28","first-page":"1492","article-title":"Predictive entropy search for multi-objective Bayesian optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hern\u00e1ndez-Lobato"},{"key":"ref29","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume-title":"Proc. 24th Int. Conf. Neural Inf. Process. Syst.","author":"Bergstra"},{"key":"ref30","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bergstra"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1820003116"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2021.2162"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1056562461"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.2307\/3318418"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010086427957"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.109901"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(90)90122-b"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(94)00035-T"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asv002"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-8847-1_5"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/0047-259X(82)90065-3"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(95)00034-8"},{"key":"ref44","first-page":"1","article-title":"A note on metric properties for some divergence measures: The Gaussian case","volume-title":"Proc. Asian Conf. Mach. Learn.","author":"Abou-Moustafa"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1162\/EVCO_a_00042"},{"volume-title":"Multiobjective Problem Solving From Nature: From Concepts to Applications","year":"2007","author":"Knowles","key":"ref46"},{"key":"ref47","article-title":"Optimal subsampling for large sample ridge regression","author":"Chen","year":"2022","journal-title":"arXiv:2204.04776"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1002\/9780470770801"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586189"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1209\/0295-5075\/19\/6\/002"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177706205"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67661-2_40"},{"key":"ref53","first-page":"918","article-title":"Predictive entropy search for efficient global optimization of black-box functions","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","volume":"1","author":"Hern\u00e1ndez-Lobato"},{"key":"ref54","article-title":"A conceptual introduction to Hamiltonian Monte Carlo","author":"Betancourt","year":"2017","journal-title":"arXiv:1701.02434"},{"key":"ref55","article-title":"A literature survey of benchmark functions for global optimization problems","author":"Jamil","year":"2013","journal-title":"arXiv:1308.4008"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/BF00143556"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"ref58","first-page":"7317","article-title":"Knowing the what but not the where in Bayesian optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nguyen"},{"key":"ref59","first-page":"8952","article-title":"Accurate uncertainty estimation and decomposition in ensemble learning","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1137\/S0036141096303359"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2638586"},{"key":"ref63","article-title":"On the convergence of Hamiltonian Monte Carlo","author":"Durmus","year":"2017","journal-title":"arXiv:1705.00166"},{"key":"ref64","article-title":"Continuously tempered Hamiltonian Monte Carlo","author":"Graham","year":"2017","journal-title":"arXiv:1704.03338"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3150\/18-BEJ1083"},{"key":"ref66","first-page":"2093","article-title":"Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem","volume-title":"Proc. Conf. Learn. Theory","author":"Wibisono"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3068107"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/VLSI.2008.54"},{"key":"ref69","first-page":"1","article-title":"REscope: High-dimensional statistical circuit simulation towards full failure region coverage","volume-title":"Proc. 51st ACM\/EDAC\/IEEE Design Autom. Conf. (DAC)","author":"Wu"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/LATW.2015.7102505"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065704001899"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1080\/08982112.2015.1100447"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRev.106.620"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1214\/19-AAP1528"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1214\/aoap\/1019737664"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2013.2245941"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2017.2768826"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2983061"},{"key":"ref79","article-title":"A tutorial on Bayesian optimization","author":"Frazier","year":"2018","journal-title":"arXiv:1807.02811"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/BF01069961"},{"key":"ref81","article-title":"A generalized framework for active learning reliability: Survey and benchmark","author":"Moustapha","year":"2021","journal-title":"arXiv:2106.01713"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/8856\/10839176\/10720041-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/10839176\/10720041.pdf?arnumber=10720041","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T19:14:26Z","timestamp":1742843666000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10720041\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":81,"URL":"https:\/\/doi.org\/10.1109\/tase.2024.3475951","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"type":"print","value":"1545-5955"},{"type":"electronic","value":"1558-3783"}],"subject":[],"published":{"date-parts":[[2025]]}}}