{"id":"https://openalex.org/W7162649598","doi":"https://doi.org/10.48550/arxiv.2605.28309","title":"Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization","display_name":"Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162649598","doi":"https://doi.org/10.48550/arxiv.2605.28309"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.28309","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137212677","display_name":"Sara Gjorgjieva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gjorgjieva, Sara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050579174","display_name":"Eva Tuba","orcid":"https://orcid.org/0000-0003-4866-9048"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuba, Eva","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137227685","display_name":"Tome Eftimov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eftimov, Tome","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.18449999392032623,"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"}},"topics":[{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.18449999392032623,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.1843000054359436,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.10520000010728836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.6972000002861023},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6868000030517578},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.565500020980835},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5591999888420105},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5546000003814697},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4803999960422516},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.38749998807907104},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3686999976634979}],"concepts":[{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.6972000002861023},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6868000030517578},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858000159263611},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.565500020980835},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5591999888420105},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5546000003814697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5278000235557556},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4803999960422516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4300000071525574},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3817000091075897},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3686999976634979},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28309","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28309","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.6396058797836304,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"large-scale":[1],"benchmarking":[2],"of":[3,40,52,102,144],"stochastic":[4],"optimization":[5],"algorithms,":[6],"the":[7,49,126,156],"key":[8],"challenge":[9],"is":[10,98],"no":[11],"longer":[12],"whether":[13,94],"repeated":[14],"runs":[15,53,67],"are":[16,119],"needed":[17],"for":[18],"reliability,":[19],"but":[20],"how":[21],"to":[22,92],"determine":[23],"when":[24],"sufficient":[25],"evidence":[26],"has":[27],"been":[28],"collected":[29],"without":[30],"incurring":[31],"unnecessary":[32],"computational":[33],"cost.":[34],"We":[35,108],"study":[36],"a":[37,41,95,113,139],"learning-based":[38],"extension":[39],"recent":[42],"empirical":[43],"online":[44],"heuristic":[45],"that":[46,132],"adaptively":[47],"estimates":[48,104,146],"required":[50],"number":[51],"using":[54,112],"outlier":[55],"handling":[56],"and":[57,87,89,121],"skewness-based":[58],"symmetry":[59],"checks.":[60],"Using":[61],"annotated":[62],"outcomes":[63],"from":[64],"132{,}000":[65],"Nevergrad":[66],"on":[68,83,123],"COCO":[69],"(24":[70],"problems":[71],"in":[72,138],"20":[73],"dimensions,":[74],"10":[75],"instances":[76],"each,":[77],"11":[78],"optimizers),":[79],"we":[80],"train":[81],"classifiers":[82],"23":[84],"statistical,":[85],"energy-free,":[86],"shape":[88],"stability":[90],"features":[91],"predict":[93],"run-number":[96,133],"estimate":[97],"reliable,":[99],"prioritizing":[100],"detection":[101,143],"incorrect":[103],"via":[105],"minority-class":[106,149],"recall.":[107],"evaluate":[109],"reliability":[110,134],"prediction":[111],"within-configuration":[114,140],"learning":[115],"setup,":[116],"where":[117],"models":[118],"trained":[120],"tested":[122],"data":[124,158],"sharing":[125],"same":[127],"optimizer.":[128],"The":[129],"results":[130],"show":[131],"can":[135],"be":[136],"learned":[137],"scenario,":[141],"enabling":[142],"unreliable":[145],"with":[147],"high":[148],"recall,":[150],"although":[151],"performance":[152],"remains":[153],"limited":[154],"by":[155],"restricted":[157],"diversity":[159],"within":[160],"fixed":[161],"configurations.":[162]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}