{"id":"https://openalex.org/W7101582011","doi":"https://doi.org/10.48550/arxiv.2510.21788","title":"Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making","display_name":"Online Mixture of Experts: No-Regret Learning for Optimal Collective Decision-Making","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7101582011","doi":"https://doi.org/10.48550/arxiv.2510.21788"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.21788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21788","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.2510.21788","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Liu, Larkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Larkin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Etesami, Jalal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Etesami, Jalal","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":true,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8919000029563904,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.8919000029563904,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.04619999974966049,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.007799999788403511,"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/regret","display_name":"Regret","score":0.7206000089645386},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.698199987411499},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.5386000275611877},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5141000151634216},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.47909998893737793},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3813999891281128},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.3718000054359436}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7206000089645386},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.698199987411499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869999766349792},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.5386000275611877},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5189999938011169},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.47909998893737793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44369998574256897},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32249999046325684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31610000133514404},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2833999991416931},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C153668964","wikidata":"https://www.wikidata.org/wiki/Q27636","display_name":"Majority rule","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.21788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21788","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.2510.21788","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.21788","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":[{"score":0.8158447742462158,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,43,87],"explore":[1],"the":[2,75,92,96,119,135,146,153,172,175],"use":[3],"of":[4,25,40,79,122,125,142,149,174],"expert-guided":[5],"bandit":[6,97],"learning,":[7],"which":[8],"we":[9],"refer":[10],"to":[11,30,34,47,83,118,151,169],"as":[12],"online":[13,71,120],"mixture-of-experts":[14],"(OMoE).":[15],"In":[16],"this":[17,49],"setting,":[18],"given":[19],"a":[20,22,109,123],"context,":[21],"candidate":[23],"committee":[24,148],"experts":[26,143,150,168],"must":[27],"determine":[28],"how":[29],"aggregate":[31,41,55,177],"their":[32,84],"outputs":[33],"achieve":[35],"optimal":[36,147],"results":[37,104,158],"in":[38,95],"terms":[39],"accuracy.":[42],"propose":[44],"two":[45],"algorithms":[46],"address":[48],"problem.":[50],"The":[51,66],"first":[52],"algorithm":[53,68],"combines":[54],"voting":[56,77],"with":[57],"UCB-driven":[58],"successive":[59],"elimination,":[60],"efficiently":[61],"pruning":[62],"suboptimal":[63],"exploration":[64],"actions.":[65],"second":[67],"employs":[69],"an":[70,176],"weighted-majority-voting":[72],"mechanism,":[73],"leveraging":[74],"respective":[76],"power":[78],"each":[80,133],"expert":[81,126],"proportional":[82],"predictive":[85],"power.":[86],"derive":[88],"theoretical":[89],"guarantees":[90,164],"for":[91,165],"regret":[93],"properties":[94],"setting":[98],"under":[99],"ideal":[100],"circumstances,":[101],"and":[102,162],"empirical":[103],"are":[105,116],"provided":[106],"accordingly.":[107],"As":[108],"modern":[110],"study":[111],"on":[112,171],"applications,":[113],"these":[114],"methods":[115],"applied":[117],"fine-tuning":[121],"set":[124,141],"large":[127],"language":[128],"models":[129],"(LLMs),":[130],"where":[131],"after":[132],"response,":[134],"generative":[136],"LLM":[137],"dynamically":[138],"reweighs":[139],"its":[140],"and/or":[144],"selects":[145],"generate":[152],"most":[154],"accurate":[155],"response.":[156],"Our":[157],"introduce":[159],"new":[160],"methodologies":[161],"no-regret":[163],"combining":[166],"multiple":[167],"improve":[170],"performance":[173],"model":[178],"overall.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-29T00:00:00"}
