{"id":"https://openalex.org/W7169827116","doi":"https://doi.org/10.48550/arxiv.2607.16986","title":"Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems","display_name":"Periodic Bootstrap Thompson Sampling For Periodically Non-Stationary Bandit Problems","publication_year":2026,"publication_date":"2026-07-18","ids":{"openalex":"https://openalex.org/W7169827116","doi":"https://doi.org/10.48550/arxiv.2607.16986"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.16986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16986","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.16986","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113392080","display_name":"Boning Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shao, Boning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5113392080"],"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9695000052452087,"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":0.9695000052452087,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.0052999998442828655,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.002899999963119626,"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/regret","display_name":"Regret","score":0.7257999777793884},{"id":"https://openalex.org/keywords/synchronizing","display_name":"Synchronizing","score":0.6273999810218811},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6270999908447266},{"id":"https://openalex.org/keywords/reset","display_name":"Reset (finance)","score":0.5619999766349792},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5278000235557556},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4625999927520752},{"id":"https://openalex.org/keywords/extension","display_name":"Extension (predicate logic)","score":0.36230000853538513}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7257999777793884},{"id":"https://openalex.org/C162932704","wikidata":"https://www.wikidata.org/wiki/Q1058791","display_name":"Synchronizing","level":3,"score":0.6273999810218811},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6270999908447266},{"id":"https://openalex.org/C2779795794","wikidata":"https://www.wikidata.org/wiki/Q7315343","display_name":"Reset (finance)","level":2,"score":0.5619999766349792},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5278000235557556},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4950000047683716},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4625999927520752},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4433000087738037},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4366999864578247},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3783000111579895},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C2781291010","wikidata":"https://www.wikidata.org/wiki/Q178580","display_name":"Period (music)","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32839998602867126},{"id":"https://openalex.org/C98644592","wikidata":"https://www.wikidata.org/wiki/Q184743","display_name":"Periodic function","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.3037000000476837},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C112353543","wikidata":"https://www.wikidata.org/wiki/Q5468162","display_name":"Forecast period","level":4,"score":0.28369998931884766},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.27239999175071716}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.16986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16986","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,"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.16986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16986","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,"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"},"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":{"This":[0],"paper":[1],"introduces":[2,143],"Periodic":[3],"Bootstrap":[4],"Thompson":[5,14],"Sampling":[6,15],"(PBTS),":[7],"an":[8],"innovative":[9],"extension":[10],"of":[11,116],"the":[12,114],"classic":[13],"(TS)":[16],"algorithm":[17],"tailored":[18],"for":[19],"bandit":[20,149],"problems":[21],"with":[22,48,83],"periodic":[23,89,107,126,152],"non-stationarity.":[24],"Conventional":[25],"TS":[26,105],"accumulates":[27],"all":[28],"past":[29],"observations,":[30],"leading":[31],"to":[32,147],"biased":[33],"posteriors":[34],"when":[35],"reward":[36,80,153],"distributions":[37],"cycle":[38],"over":[39],"time.":[40],"PBTS":[41,68,94,142],"overcomes":[42],"this":[43],"by":[44],"synchronizing":[45],"belief":[46],"resets":[47],"known":[49],"or":[50],"inferred":[51],"period":[52],"intervals":[53],"and":[54,78,87,128,139],"embedding":[55],"structured":[56],"bootstrap":[57,85,140],"exploration":[58],"phases,":[59],"effectively":[60],"purging":[61],"obsolete":[62],"data":[63],"while":[64],"preserving":[65],"uncertainty":[66],"estimates.":[67],"is":[69],"tested":[70],"in":[71,100,106,151],"artificially":[72],"constructed":[73],"environments,":[74],"which":[75],"include":[76],"skewed":[77],"balanced":[79],"distributions,":[81],"along":[82],"different":[84],"proportions":[86],"misaligned":[88],"intervals.":[90],"Results":[91],"indicate":[92],"that":[93],"generally":[95],"achieves":[96],"statistically":[97],"significant":[98],"reductions":[99],"cumulative":[101],"regret":[102],"against":[103],"traditional":[104],"non-stationary":[108],"environments.":[109],"Subsequent":[110],"discussion":[111],"further":[112],"articulates":[113],"potential":[115],"PBTS's":[117],"real-world":[118],"deployment.":[119],"The":[120],"study":[121],"mentions":[122],"limitations":[123],"like":[124],"extreme":[125],"misalignment":[127],"proposes":[129],"future":[130],"research":[131],"such":[132],"as":[133],"self-adjusting":[134],"cycle-recognition.":[135],"With":[136],"memory":[137],"reset":[138],"phase,":[141],"a":[144],"novel":[145],"approach":[146],"optimizing":[148],"algorithms":[150],"contexts.":[154]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-22T00:00:00"}