{"id":"https://openalex.org/W7163703019","doi":"https://doi.org/10.48550/arxiv.2606.06043","title":"Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader","display_name":"Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163703019","doi":"https://doi.org/10.48550/arxiv.2606.06043"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.06043","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06043","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.06043","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137927857","display_name":"Jongyeong Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jongyeong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137954312","display_name":"Junya Honda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Honda, Junya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137946156","display_name":"Shinji Ito","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ito, Shinji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129077717","display_name":"Chansoo Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Chansoo","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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9609000086784363,"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":0.9609000086784363,"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":0.021400000900030136,"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/T13553","display_name":"Age of Information Optimization","score":0.003100000089034438,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5932000279426575},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.4706000089645386},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.4147000014781952},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.3675999939441681},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.2825999855995178},{"id":"https://openalex.org/keywords/pareto-optimal","display_name":"Pareto optimal","score":0.28110000491142273}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5957000255584717},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5932000279426575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5575000047683716},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.45559999346733093},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39320001006126404},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32510000467300415},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28949999809265137},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.06043","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06043","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.06043","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06043","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Follow-the-regularized-leader":[0],"framework":[1],"has":[2],"shown":[3],"effectiveness":[4],"and":[5,170,185],"flexibility":[6],"in":[7,27,44,151,179],"online":[8],"learning":[9,15,24,73,84,110,149,186],"problems,":[10],"where":[11],"the":[12,30,68,99,104,117,142,152,161,171],"choice":[13],"of":[14,29,70,137,164,176],"rates":[16,25,74,111,150],"are":[17],"known":[18],"to":[19,134],"be":[20,95,175],"crucial.":[21],"Recently,":[22],"adaptive":[23,83,148],"defined":[26],"terms":[28],"arm-selection":[31],"probabilities,":[32],"obtained":[33],"by":[34,88],"solving":[35],"convex":[36],"optimization,":[37],"have":[38],"achieved":[39],"improved":[40],"best-of-both-worlds":[41],"(BOBW)":[42],"guarantees":[43,51,144],"various":[45],"bandit":[46,153],"problems.":[47],"In":[48],"contrast,":[49],"BOBW":[50,118,143],"for":[52,86,120,125,145],"its":[53,63],"computationally":[54],"efficient":[55],"alternative,":[56],"follow-the-perturbed-leader":[57],"(FTPL),":[58],"remain":[59],"relatively":[60],"limited":[61],"since":[62],"optimization-free":[64],"nature":[65],"ironically":[66],"makes":[67],"design":[69],"adaptive,":[71],"probability-dependent":[72,168],"non-trivial.":[75],"To":[76],"address":[77],"this":[78],"challenge,":[79],"we":[80,115],"propose":[81],"an":[82],"rate":[85,187],"FTPL":[87,121,146,165,184],"introducing":[89],"surrogate":[90,113],"probability":[91],"functions":[92],"that":[93],"can":[94],"computed":[96],"only":[97],"from":[98],"available":[100],"quantities,":[101],"without":[102],"requiring":[103],"exact":[105],"probabilities.":[106],"Based":[107],"on":[108],"these":[109],"with":[112,122,147,155],"functions,":[114],"provide":[116],"guarantee":[119],"Pareto":[123],"perturbations":[124],"any":[126],"shape":[127],"parameter":[128],"$\u03b1&gt;1$,":[129],"generalizing":[130],"prior":[131],"results":[132],"restricted":[133],"specific":[135],"choices":[136],"$\u03b1=2$.":[138],"We":[139],"further":[140],"show":[141],"problem":[154],"expert":[156],"advices.":[157],"Our":[158],"approach":[159],"preserves":[160],"computational":[162],"simplicity":[163],"while":[166],"enabling":[167],"adaptivity,":[169],"surrogate-based":[172],"methodology":[173],"may":[174],"independent":[177],"interest":[178],"other":[180],"algorithmic":[181],"frameworks":[182],"beyond":[183],"designs.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-06T00:00:00"}
