{"id":"https://openalex.org/W7123462636","doi":"https://doi.org/10.48550/arxiv.2601.06813","title":"Analyzing the effect of prediction accuracy on the distributionally-robust competitive ratio","display_name":"Analyzing the effect of prediction accuracy on the distributionally-robust competitive ratio","publication_year":2026,"publication_date":"2026-01-11","ids":{"openalex":"https://openalex.org/W7123462636","doi":"https://doi.org/10.48550/arxiv.2601.06813"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.06813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06813","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.06813","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104117194","display_name":"Toru Yoshinaga","orcid":"https://orcid.org/0009-0003-4725-1501"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yoshinaga, Toru","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122915413","display_name":"Yasushi Kawase","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kawase, Yasushi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104117194"],"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/T12288","display_name":"Optimization and Search Problems","score":0.5782999992370605,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12288","display_name":"Optimization and Search Problems","score":0.5782999992370605,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.25049999356269836,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.031700000166893005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6121000051498413},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.5947999954223633},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.555400013923645},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5242999792098999},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4900999963283539},{"id":"https://openalex.org/keywords/infimum-and-supremum","display_name":"Infimum and supremum","score":0.4896000027656555},{"id":"https://openalex.org/keywords/competitive-analysis","display_name":"Competitive analysis","score":0.48579999804496765},{"id":"https://openalex.org/keywords/monotone-polygon","display_name":"Monotone polygon","score":0.4546000063419342},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.43140000104904175}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.5947999954223633},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5891000032424927},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.555400013923645},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5242999792098999},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5127000212669373},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C95611797","wikidata":"https://www.wikidata.org/wiki/Q17502105","display_name":"Infimum and supremum","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C102408133","wikidata":"https://www.wikidata.org/wiki/Q5156350","display_name":"Competitive analysis","level":3,"score":0.48579999804496765},{"id":"https://openalex.org/C2834757","wikidata":"https://www.wikidata.org/wiki/Q4925424","display_name":"Monotone polygon","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43160000443458557},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.43140000104904175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4081000089645386},{"id":"https://openalex.org/C63002673","wikidata":"https://www.wikidata.org/wiki/Q2260590","display_name":"Scoring rule","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C108882938","wikidata":"https://www.wikidata.org/wiki/Q274506","display_name":"Uniform distribution (continuous)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.06813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06813","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":"doi:10.48550/arxiv.2601.06813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06813","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,89],"field":[1],"of":[2,35,41,164],"algorithms":[3],"with":[4],"predictions":[5,15,27],"aims":[6],"to":[7,63,173,193,204,216,241],"improve":[8,29,243],"algorithm":[9,17],"performance":[10,69,246],"by":[11,84],"integrating":[12],"machine":[13],"learning":[14],"into":[16],"design.":[18],"A":[19,123],"central":[20],"question":[21],"in":[22,185,201],"this":[23,36,45,143,186],"area":[24],"is":[25,38,49,91,108,127,158],"how":[26],"can":[28],"performance,":[30],"and":[31,101,120,161,177,181],"a":[32,52,68,159,174,198,218,224],"key":[33],"aspect":[34],"analysis":[37],"the":[39,60,64,78,94,98,102,106,111,117,133,148,153,170,194,206,213,228,233,238,245],"role":[40],"prediction":[42,47,73,119,138,165,209],"accuracy.":[43,166],"In":[44],"context,":[46],"accuracy":[48,121,139,235,250],"defined":[50,92,231],"as":[51,93,137,232],"guaranteed":[53],"probability":[54],"that":[55,71,115,147,179],"an":[56],"instance":[57,113],"drawn":[58],"from":[59],"distribution":[61,114],"belongs":[62],"predicted":[65],"set.":[66],"As":[67],"measure":[70],"incorporates":[72],"accuracy,":[74,230],"we":[75,145,189,222],"focus":[76],"on":[77,142,208],"distributionally-robust":[79],"competitive":[80],"ratio":[81,96],"(DRCR),":[82],"introduced":[83],"Sun":[85],"et":[86],"al.~(ICML":[87],"2024).":[88],"DRCR":[90,134,150,171,215,240],"expected":[95],"between":[97],"algorithm's":[99],"cost":[100],"optimal":[103,149,214,239],"cost,":[104],"where":[105],"expectation":[107],"taken":[109],"over":[110,155],"worst-case":[112],"satisfies":[116],"given":[118],"requirement.":[122],"known":[124],"structural":[125],"property":[126],"that,":[128],"for":[129,212,226,237],"any":[130,249],"fixed":[131],"algorithm,":[132],"decreases":[135],"linearly":[136],"increases.":[140],"Building":[141],"result,":[144],"establish":[146],"value":[151],"(i.e.,":[152],"infimum":[154],"all":[156],"algorithms)":[157],"monotone":[160],"concave":[162],"function":[163],"We":[167],"further":[168],"generalize":[169],"framework":[172],"multiple-prediction":[175],"setting":[176],"show":[178],"monotonicity":[180],"concavity":[182],"are":[183],"preserved":[184],"setting.":[187],"Finally,":[188],"apply":[190],"our":[191],"results":[192],"ski":[195],"rental":[196],"problem,":[197],"benchmark":[199],"problem":[200],"online":[202],"optimization,":[203],"identify":[205],"conditions":[207],"accuracies":[210],"required":[211,236],"attain":[217],"target":[219],"value.":[220],"Moreover,":[221],"provide":[223],"method":[225],"computing":[227],"critical":[229],"minimum":[234],"strictly":[242],"upon":[244],"attainable":[247],"without":[248],"guarantee.":[251]},"counts_by_year":[],"updated_date":"2026-01-14T23:44:37.837170","created_date":"2026-01-14T00:00:00"}