{"id":"https://openalex.org/W3047247240","doi":"https://doi.org/10.1137/19m1272676","title":"Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise","display_name":"Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3047247240","doi":"https://doi.org/10.1137/19m1272676","mag":"3047247240"},"language":"en","primary_location":{"id":"doi:10.1137/19m1272676","is_oa":false,"landing_page_url":"https://doi.org/10.1137/19m1272676","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://scholars.cityu.edu.hk/en/publications/bayesian-optimization-of-expected-quadratic-loss-for-multirespons","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067414010","display_name":"Matthias Hwai Yong Tan","orcid":"https://orcid.org/0000-0002-4823-7539"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Matthias H. Y. Tan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5067414010"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8953,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.89065938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"8","issue":"3","first_page":"891","last_page":"925"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/latin-hypercube-sampling","display_name":"Latin hypercube sampling","score":0.6736262440681458},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5845504999160767},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.548102617263794},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5328875780105591},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.527260422706604},{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.4650168716907501},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.43997621536254883},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.43924248218536377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4205884337425232},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2216055691242218},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.17029109597206116},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.11148318648338318},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11010843515396118}],"concepts":[{"id":"https://openalex.org/C20820323","wikidata":"https://www.wikidata.org/wiki/Q6496514","display_name":"Latin hypercube sampling","level":3,"score":0.6736262440681458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845504999160767},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.548102617263794},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5328875780105591},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.527260422706604},{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.4650168716907501},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.43997621536254883},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43924248218536377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4205884337425232},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2216055691242218},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.17029109597206116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11148318648338318},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11010843515396118},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1137/19m1272676","is_oa":false,"landing_page_url":"https://doi.org/10.1137/19m1272676","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},{"is_oa":true,"landing_page_url":"https://scholars.cityu.edu.hk/en/publications/bayesian-optimization-of-expected-quadratic-loss-for-multirespons","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:publications/3eb1e84a-aa18-4a8b-a510-41b2c6085c37","is_oa":true,"landing_page_url":"https://hdl.handle.net/2031/3eb1e84a-aa18-4a8b-a510-41b2c6085c37","pdf_url":null,"source":{"id":"https://openalex.org/S7407055387","display_name":"CityU Scholars","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Tan, M H Y 2020, 'Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise', SIAM / ASA Journal on Uncertainty Quantification, vol. 8, no. 3, pp. 891-925. https://doi.org/10.1137/19M1272676","raw_type":"article"}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://scholars.cityu.edu.hk/en/publications/bayesian-optimization-of-expected-quadratic-loss-for-multirespons","pdf_url":null,"source":{"id":"https://openalex.org/S2911293512","display_name":"SIAM/ASA Journal on Uncertainty Quantification","issn_l":"2166-2525","issn":["2166-2525"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM/ASA Journal on Uncertainty Quantification","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3729859358","display_name":null,"funder_award_id":"T32-101/15-R","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"}],"funders":[{"id":"https://openalex.org/F4320321592","display_name":"Research Grants Council, University Grants Committee","ror":"https://ror.org/00djwmt25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W32193604","https://openalex.org/W1018941212","https://openalex.org/W1510052597","https://openalex.org/W1550183740","https://openalex.org/W1972259045","https://openalex.org/W1976383206","https://openalex.org/W1990570841","https://openalex.org/W2004808074","https://openalex.org/W2018044188","https://openalex.org/W2020732830","https://openalex.org/W2026733770","https://openalex.org/W2030006024","https://openalex.org/W2040932457","https://openalex.org/W2059795119","https://openalex.org/W2063180182","https://openalex.org/W2076474160","https://openalex.org/W2083027289","https://openalex.org/W2093006188","https://openalex.org/W2094040025","https://openalex.org/W2098720801","https://openalex.org/W2155927283","https://openalex.org/W2158237256","https://openalex.org/W2160605723","https://openalex.org/W2185487981","https://openalex.org/W2268565176","https://openalex.org/W2314014504","https://openalex.org/W2323351731","https://openalex.org/W2779381128","https://openalex.org/W2951769871","https://openalex.org/W3102249914","https://openalex.org/W3125143846","https://openalex.org/W3193009655","https://openalex.org/W4206341000","https://openalex.org/W4297776957"],"related_works":["https://openalex.org/W2234994522","https://openalex.org/W2066255661","https://openalex.org/W3151955386","https://openalex.org/W3125393362","https://openalex.org/W2594425114","https://openalex.org/W3125374017","https://openalex.org/W2086712468","https://openalex.org/W2575206153","https://openalex.org/W2015140027","https://openalex.org/W3144982195"],"abstract_inverted_index":{"Design":[0],"of":[1,30,149],"systems":[2],"based":[3,26,53,144,166,196],"on":[4,27,54,145,167,197],"computer":[5,45],"simulations":[6,46],"is":[7,21,38,63,115],"prevalent.":[8],"An":[9],"important":[10],"idea":[11],"to":[12,22,40,76,152,159],"improve":[13],"design":[14,19,37,81,174],"quality,":[15],"called":[16],"robust":[17,39],"parameter":[18],"(RPD),":[20],"optimize":[23],"control":[24],"factors":[25,165],"the":[28,36,51,61,79,85,92,108,116,133,146,150,157,199,219],"expectation":[29],"a":[31,55,64,168,203],"loss":[32,111,119,201],"function":[33,120,148],"so":[34,83],"that":[35,84,182],"noise":[41,164],"factor":[42],"variations.":[43],"When":[44],"are":[47,74,216],"time":[48],"consuming,":[49],"optimizing":[50,132],"simulator":[52],"Gaussian":[56],"process":[57],"(GP)":[58],"emulator":[59,87],"for":[60,101,121,131,176,212],"response":[62],"computationally":[65],"efficient":[66],"approach.":[67],"For":[68],"this":[69],"purpose,":[70],"acquisition":[71],"functions":[72],"(AFs)":[73],"used":[75],"sequentially":[77],"determine":[78],"next":[80],"point":[82],"GP":[86,169,204],"can":[88],"more":[89],"accurately":[90],"locate":[91],"optimal":[93],"setting.":[94],"Despite":[95],"this,":[96],"few":[97],"articles":[98],"consider":[99],"AFs":[100,130,158,185],"positive":[102],"definite":[103],"quadratic":[104,110,200],"forms":[105],"such":[106,177],"as":[107,202],"expected":[109,118],"(EQL)":[112],"function,":[113],"which":[114],"standard":[117],"RPD":[122,160],"with":[123,162],"nominally-the-best":[124],"responses.":[125],"This":[126],"paper":[127],"proposes":[128],"new":[129],"EQL,":[134],"analyzes":[135],"their":[136],"convergence,":[137],"and":[138,141,171,190,205],"develops":[139],"quick":[140],"accurate":[142],"methods":[143],"characteristic":[147],"EQL":[151],"compute":[153],"them.":[154],"We":[155],"apply":[156],"problems":[161],"internal":[163],"model":[170],"an":[172,193],"initial":[173],"tailored":[175],"problems.":[178],"Numerical":[179],"results":[180,215],"indicate":[181],"all":[183],"four":[184],"considered":[186],"have":[187],"similar":[188],"performance,":[189],"they":[191],"outperform":[192],"optimization":[194],"approach":[195],"modeling":[198],"maximin":[206],"Latin":[207],"hypercube":[208],"designs.":[209],"MATLAB":[210],"codes":[211],"reproducing":[213],"reported":[214],"given":[217],"in":[218],"online":[220],"supplement.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7}],"updated_date":"2026-05-17T08:19:37.847499","created_date":"2025-10-10T00:00:00"}