{"id":"https://openalex.org/W7207846750","doi":"https://doi.org/10.48550/arxiv.2609.01761","title":"Pooling and Drift in Delayed Bandits","display_name":"Pooling and Drift in Delayed Bandits","publication_year":2026,"publication_date":"2026-09-01","ids":{"openalex":"https://openalex.org/W7207846750","doi":"https://doi.org/10.48550/arxiv.2609.01761"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.01761","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.01761","pdf_url":"https://arxiv.org/pdf/2609.01761","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.01761","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135570469","display_name":"Melika Baghi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Baghi, Melika","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5135570469"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":1.0,"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":1.0,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0,"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/T12288","display_name":"Optimization and Search Problems","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2614","display_name":"Theoretical Computer Science"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/regret-bounds","display_name":"regret bounds","score":0.574999988079071},{"id":"https://openalex.org/keywords/state-channels","display_name":"state channels","score":0.3700000047683716},{"id":"https://openalex.org/keywords/effective-dimension","display_name":"effective dimension","score":0.6510000228881836},{"id":"https://openalex.org/keywords/multi-armed-bandits","display_name":"multi-armed bandits","score":0.5960000157356262}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7695000171661377},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.661300003528595},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5852000117301941},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.48840001225471497},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45399999618530273},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.44670000672340393},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4036000072956085},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3091999888420105},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.30390000343322754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.01761","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.01761","pdf_url":"https://arxiv.org/pdf/2609.01761","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.01761","doi":"https://doi.org/10.48550/arxiv.2609.01761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.01761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.01761","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.01761","pdf_url":"https://arxiv.org/pdf/2609.01761","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7207846750.pdf","grobid_xml":"https://content.openalex.org/works/W7207846750.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W4414856347","https://openalex.org/W4417122008","https://openalex.org/W7162143396"],"related_works":["https://openalex.org/W3211407287","https://openalex.org/W4378945443","https://openalex.org/W3135449132","https://openalex.org/W3209843109","https://openalex.org/W2840564675","https://openalex.org/W2947611399","https://openalex.org/W4412208995","https://openalex.org/W4406449487","https://openalex.org/W2977303077","https://openalex.org/W2952973898"],"abstract_inverted_index":{"A":[0],"system":[1],"often":[2],"has":[3],"to":[4,55,202,215],"act":[5,12],"long":[6],"before":[7],"it":[8,73],"learns":[9],"whether":[10],"the":[11,35,63,67,71,86,90,100,122,137,153,163,178,189,195,210],"worked:":[13],"a":[14,17,22,30,48,131,219],"recommender":[15],"sees":[16],"click":[18],"in":[19,24,141,147],"seconds":[20],"and":[21,29,89,121,126,134,181],"purchase":[23],"days.":[25],"With":[26],"$K$":[27],"actions":[28,101,108],"delay":[31],"of":[32,124],"$d$":[33,167],"rounds,":[34,46],"best":[36],"rate":[37],"known":[38],"for":[39,130,136,143],"this":[40,113],"setting":[41],"is":[42,51,92],"$\\widetilde{O}(\\sqrt{(K+d)T})$":[43],"over":[44],"$T$":[45],"so":[47],"longer":[49],"menu":[50],"always":[52],"more":[53],"expensive":[54],"learn":[56],"from.":[57],"It":[58],"need":[59],"not":[60],"be:":[61],"if":[62],"outcome":[64,78],"depends":[65],"on":[66,209],"action":[68,81],"only":[69],"through":[70],"state":[72,196],"produced,":[74],"then":[75],"one":[76],"late":[77],"informs":[79],"every":[80],"that":[82],"could":[83],"have":[84],"produced":[85],"observed":[87],"state,":[88],"price":[91,154],"set":[93],"by":[94,105,200,213],"how":[95,106,184],"many":[96,107],"genuinely":[97],"different":[98],"states":[99,151],"produce":[102],"rather":[103],"than":[104],"there":[109],"are.":[110],"We":[111],"measure":[112],"using":[114],"an":[115,157],"effective":[116],"dimension":[117],"$v_t$":[118],"between":[119],"$1$":[120],"number":[123],"states,":[125],"prove":[127],"$\\widetilde{O}(\\sqrt{(d+1)V\\log":[128],"K})$":[129],"rotating":[132],"algorithm":[133,139,171],"$\\widetilde{O}(\\sqrt{V^{-}}+\\sqrt{dT})$":[135],"single-copy":[138],"used":[140],"practice,":[142],"any":[144],"budget":[145],"fixed":[146],"advance;":[148],"merging":[149],"similar":[150],"lowers":[152],"further,":[155],"at":[156],"explicit":[158],"bias.":[159],"Even":[160],"when":[161],"given":[162],"exact":[164],"losses":[165,186],"from":[166],"rounds":[168],"ago,":[169],"no":[170],"escapes":[172],"$\u03a9(\\sqrt{dE\\min\\{1+\\log":[173],"J,T/d\\}})$,":[174],"where":[175],"$J$":[176],"counts":[177],"drifting":[179],"directions":[180],"$E$":[182],"bounds":[183],"far":[185],"move":[187],"while":[188],"learner":[190],"waits.":[191],"On":[192],"generated":[193],"data,":[194],"channel":[197],"cuts":[198],"regret":[199],"up":[201],"79":[203],"percent":[204,217],"against":[205,218],"action-level":[206],"weighting":[207],"and,":[208],"funnel":[211],"family,":[212],"32":[214],"68":[216],"tuned":[220],"minimax-optimal":[221],"method.":[222]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-04T00:00:00"}