{"id":"https://openalex.org/W7220925837","doi":"https://doi.org/10.48550/arxiv.2610.10340","title":"Data Reuse in Non-Stationary Learning","display_name":"Data Reuse in Non-Stationary Learning","publication_year":2026,"publication_date":"2026-10-07","ids":{"openalex":"https://openalex.org/W7220925837","doi":"https://doi.org/10.48550/arxiv.2610.10340"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.10340","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.10340","pdf_url":"https://arxiv.org/pdf/2610.10340","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","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.10340","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077036467","display_name":"Tomer Gafni","orcid":"https://orcid.org/0000-0002-5906-1428","observed_orcids":["https://orcid.org/0000-0002-5906-1428"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gafni, Tomer","raw_affiliation_strings":["Columbia University Columbia University Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University Columbia University Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029998274","display_name":"Garud Iyengar","orcid":"https://orcid.org/0000-0001-6546-4154","observed_orcids":["https://orcid.org/0000-0001-6546-4154"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Iyengar, Garud","raw_affiliation_strings":["Columbia University Columbia University Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University Columbia University Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071724876","display_name":"Assaf J. Zeevi","orcid":"https://orcid.org/0000-0003-1075-6664","observed_orcids":["https://orcid.org/0000-0003-1075-6664"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zeevi, Assaf","raw_affiliation_strings":["Columbia University Columbia University Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University Columbia University Columbia University","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78577930"],"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":0.9824000000953674,"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":0.9824000000953674,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.004100000020116568,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/non-stationary-learning","display_name":"non-stationary learning","score":0.8450000286102295},{"id":"https://openalex.org/keywords/e-learning","display_name":"online learning","score":0.800000011920929},{"id":"https://openalex.org/keywords/data-reuse","display_name":"data reuse","score":0.6850000023841858},{"id":"https://openalex.org/keywords/parameter-tracking","display_name":"parameter tracking","score":0.6359999775886536},{"id":"https://openalex.org/keywords/abrupt-parameter-changes","display_name":"abrupt parameter changes","score":0.5009999871253967},{"id":"https://openalex.org/keywords/bias-variance-trade-off","display_name":"bias-variance trade-off","score":0.8370000123977661},{"id":"https://openalex.org/keywords/anytime-algorithms","display_name":"anytime algorithms","score":0.7670000195503235},{"id":"https://openalex.org/keywords/online-change-detection","display_name":"online change detection","score":0.8740000128746033},{"id":"https://openalex.org/keywords/compatibility-testing","display_name":"compatibility testing","score":0.8299999833106995},{"id":"https://openalex.org/keywords/contamination-control","display_name":"contamination control","score":0.8740000128746033},{"id":"https://openalex.org/keywords/regret-analysis","display_name":"regret analysis","score":0.6769999861717224},{"id":"https://openalex.org/keywords/information-theoretic-lower-bounds","display_name":"information-theoretic lower bounds","score":0.8880000114440918},{"id":"https://openalex.org/keywords/minimax-optimality","display_name":"minimax optimality","score":0.8799999952316284}],"concepts":[{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.8205999732017517},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7653999924659729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6297000050544739},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5224999785423279},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4253999888896942},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33739998936653137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33340001106262207},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3188999891281128},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C162392398","wikidata":"https://www.wikidata.org/wiki/Q272404","display_name":"Finite set","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.10340","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.10340","pdf_url":"https://arxiv.org/pdf/2610.10340","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.2610.10340","doi":"https://doi.org/10.48550/arxiv.2610.10340","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.10340","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:2610.10340","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.10340","pdf_url":"https://arxiv.org/pdf/2610.10340","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/W7220925837.pdf","grobid_xml":"https://content.openalex.org/works/W7220925837.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7214272806","https://openalex.org/W4388067780","https://openalex.org/W3017031875","https://openalex.org/W58212224","https://openalex.org/W122993283","https://openalex.org/W2518996637","https://openalex.org/W7133238252","https://openalex.org/W2920202034","https://openalex.org/W7007789280","https://openalex.org/W7133805592"],"abstract_inverted_index":{"We":[0,112],"consider":[1],"online":[2,104],"learning":[3],"in":[4,72,116],"non-stationary":[5],"environments,":[6],"where":[7],"the":[8,28,40,44,56,69,86,114,122,129,142,152],"goal":[9],"is":[10],"to":[11,35,58,85],"track":[12],"an":[13],"unknown":[14],"parameter":[15],"that":[16,79,102,140],"switches":[17],"abruptly":[18],"between":[19],"a":[20,82,93,135],"finite":[21],"set":[22],"of":[23,30,43,49,68,75,95,124,131,145,151,155],"recurring":[24],"values.":[25],"Recurrence":[26],"opens":[27],"possibility":[29],"judiciously":[31],"reusing":[32],"past":[33],"observations":[34],"improve":[36],"algorithm":[37],"performance.":[38],"However,":[39],"changing":[41],"nature":[42],"underlying":[45],"signal":[46],"and":[47,77,109,133],"lack":[48],"information":[50],"on":[51],"these":[52],"dynamics":[53],"may":[54],"limit":[55],"ability":[57],"\"safely\"":[59],"reuse":[60],"data.":[61],"In":[62],"this":[63,73],"paper":[64],"we":[65,91],"quantify":[66],"some":[67],"fundamental":[70],"tradeoffs":[71],"class":[74,94],"problems,":[76],"show":[78],"they":[80],"bear":[81],"certain":[83],"resemblance":[84],"classical":[87],"bias-variance":[88],"dilemma.":[89],"Specifically,":[90],"propose":[92],"anytime":[96],"algorithms,":[97],"dubbed":[98],"Exposure-Capped":[99],"Reuse":[100],"(ECR),":[101],"combine":[103],"change":[105],"detection,":[106],"compatibility":[107],"testing,":[108],"\"contamination\"":[110],"control.":[111],"characterize":[113],"regime":[115],"which":[117],"ECR's":[118],"regret":[119],"scales":[120],"with":[121],"number":[123,130],"distinct":[125],"values":[126],"rather":[127],"than":[128],"changes,":[132],"derive":[134],"novel":[136],"information-theoretic":[137],"lower":[138],"bound":[139],"establishes":[141],"near-minimax":[143],"optimality":[144],"ECR.":[146],"This":[147],"provides":[148],"rigorous":[149],"quantification":[150],"statistical":[153],"\"value\"":[154],"data":[156],"reuse.":[157]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-10-09T00:00:00"}