{"id":"https://openalex.org/W4415315146","doi":"https://doi.org/10.48550/arxiv.2510.06439","title":"Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models","display_name":"Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models","publication_year":2025,"publication_date":"2025-10-07","ids":{"openalex":"https://openalex.org/W4415315146","doi":"https://doi.org/10.48550/arxiv.2510.06439"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2510.06439","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06439","pdf_url":"https://arxiv.org/pdf/2510.06439","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.06439","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084755187","display_name":"Akash Yadav","orcid":"https://orcid.org/0000-0001-5615-8884"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yadav, Akash","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090054434","display_name":"Ruda Zhang","orcid":"https://orcid.org/0000-0002-2899-1008"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruda","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084755187"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.12720000743865967,"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":0.12720000743865967,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.12300000339746475,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.11330000311136246,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.7835000157356262},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.682699978351593},{"id":"https://openalex.org/keywords/stochastic-optimization","display_name":"Stochastic optimization","score":0.5297999978065491},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/random-search","display_name":"Random search","score":0.44110000133514404},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4230000078678131},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4187000095844269},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.41530001163482666},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.39649999141693115},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.38280001282691956}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7835000157356262},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.682699978351593},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6258000135421753},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5403000116348267},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C126661757","wikidata":"https://www.wikidata.org/wiki/Q4925641","display_name":"Random search","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4154999852180481},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.38280001282691956},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3553999960422516},{"id":"https://openalex.org/C55479107","wikidata":"https://www.wikidata.org/wiki/Q97663916","display_name":"Stochastic approximation","level":3,"score":0.34869998693466187},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3434000015258789},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.34139999747276306},{"id":"https://openalex.org/C137631369","wikidata":"https://www.wikidata.org/wiki/Q7617831","display_name":"Stochastic programming","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.30869999527931213},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C109578324","wikidata":"https://www.wikidata.org/wiki/Q3354463","display_name":"Random optimization","level":5,"score":0.2773999869823456},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C94361409","wikidata":"https://www.wikidata.org/wiki/Q7882500","display_name":"Uncertainty reduction theory","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2549999952316284},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.06439","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06439","pdf_url":"https://arxiv.org/pdf/2510.06439","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.06439","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.06439","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.06439","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.06439","pdf_url":"https://arxiv.org/pdf/2510.06439","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hyperparameter":[0],"tuning":[1,38],"is":[2],"a":[3,30,42,46,58,76,96,116],"challenging":[4],"problem":[5],"especially":[6],"when":[7],"the":[8,62,70,80,83,103,124,127],"system":[9],"itself":[10],"involves":[11],"uncertainty.":[12],"Due":[13],"to":[14,115],"noisy":[15],"function":[16],"evaluations,":[17],"optimization":[18,33,101],"under":[19,39],"uncertainty":[20],"can":[21],"be":[22],"computationally":[23],"expensive.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28,74],"present":[29],"novel":[31],"Bayesian":[32],"framework":[34],"tailored":[35],"for":[36,61,79],"hyperparameter":[37],"uncertainty,":[40],"with":[41,95],"focus":[43],"on":[44],"optimizing":[45],"scale-":[47],"or":[48],"precision-type":[49],"parameter":[50],"in":[51,113,119,134],"stochastic":[52],"models.":[53],"The":[54],"proposed":[55,104,128],"method":[56,129],"employs":[57],"statistical":[59],"surrogate":[60],"underlying":[63],"random":[64,84],"variable,":[65],"enabling":[66],"analytical":[67],"evaluation":[68],"of":[69,82,126],"expectation":[71],"operator.":[72],"Moreover,":[73],"derive":[75],"closed-form":[77],"expression":[78],"optimizer":[81],"acquisition":[85],"function,":[86],"which":[87],"significantly":[88],"reduces":[89],"computational":[90,120,135],"cost":[91],"per":[92],"iteration.":[93],"Compared":[94],"conventional":[97],"one-dimensional":[98],"Monte":[99],"Carlo-based":[100],"scheme,":[102],"approach":[105],"requires":[106],"40":[107],"times":[108],"fewer":[109],"data":[110],"points,":[111],"resulting":[112],"up":[114],"40-fold":[117],"reduction":[118],"cost.":[121],"We":[122],"demonstrate":[123],"effectiveness":[125],"through":[130],"two":[131],"numerical":[132],"examples":[133],"engineering.":[136]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-18T00:00:00"}
