{"id":"https://openalex.org/W7225328797","doi":"https://doi.org/10.48550/arxiv.2611.05842","title":"PEARL: Prediction Error-Adaptive Regularized Learning for Selective Forgetting under Partial Preference Drift","display_name":"PEARL: Prediction Error-Adaptive Regularized Learning for Selective Forgetting under Partial Preference Drift","publication_year":2026,"publication_date":"2026-11-04","ids":{"openalex":"https://openalex.org/W7225328797","doi":"https://doi.org/10.48550/arxiv.2611.05842"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2611.05842","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.05842","pdf_url":"https://arxiv.org/pdf/2611.05842","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/2611.05842","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014460968","display_name":"Rashmi Gangadharaiah","orcid":"https://orcid.org/0009-0001-5502-7265","observed_orcids":["https://orcid.org/0009-0001-5502-7265"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gangadharaiah, Rashmi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123721476","display_name":"Balakrishnan Narayanaswamy","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Narayanaswamy, Balakrishnan","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":0.9994999766349792,"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":0.9994999766349792,"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":0.00019999999494757503,"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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/selective-forgetting","display_name":"selective forgetting","score":0.8859999775886536},{"id":"https://openalex.org/keywords/preference-drift","display_name":"preference drift","score":0.8529999852180481},{"id":"https://openalex.org/keywords/non-stationary-bandits","display_name":"non-stationary bandits","score":0.7839999794960022},{"id":"https://openalex.org/keywords/personalized-recommendation","display_name":"personalized recommendation","score":0.6940000057220459},{"id":"https://openalex.org/keywords/prediction-error","display_name":"prediction error","score":0.6150000095367432},{"id":"https://openalex.org/keywords/regret-analysis","display_name":"regret analysis","score":0.8790000081062317}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.8640999794006348},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7731999754905701},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5641000270843506},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5187000036239624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5170000195503235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5019999742507935},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41100001335144043},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3840999901294708},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C2779251273","wikidata":"https://www.wikidata.org/wiki/Q43436","display_name":"Pearl","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2611.05842","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.05842","pdf_url":"https://arxiv.org/pdf/2611.05842","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.2611.05842","doi":"https://doi.org/10.48550/arxiv.2611.05842","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.05842","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":"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2611.05842","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.05842","pdf_url":"https://arxiv.org/pdf/2611.05842","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":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2997528551","https://openalex.org/W2912801008","https://openalex.org/W7221771754","https://openalex.org/W7214707673","https://openalex.org/W4404781559","https://openalex.org/W4402963447","https://openalex.org/W3120218913","https://openalex.org/W4389325783","https://openalex.org/W7156305867","https://openalex.org/W7151099961"],"abstract_inverted_index":{"User":[0],"preferences":[1,26,179],"shift":[2],"as":[3,25],"needs,":[4],"context,":[5],"and":[6,17,123,126,162,185,217],"tastes":[7],"evolve.":[8],"This":[9],"matters":[10],"for":[11],"personalized":[12,147],"recommendation,":[13],"LLM":[14,148,214],"response":[15,149],"generation,":[16],"RLHF-trained":[18],"reward":[19],"models,":[20],"which":[21,80],"can":[22],"become":[23],"miscalibrated":[24],"drift.":[27],"Existing":[28],"non-stationary":[29,124],"bandit":[30,166],"methods":[31],"forget":[32],"uniformly":[33],"across":[34],"all":[35],"dimensions,":[36],"but":[37],"real":[38],"preference":[39,110],"shifts":[40],"are":[41,90,180],"typically":[42],"partial:":[43],"some":[44],"dimensions":[45],"go":[46],"stale":[47],"while":[48,93,172],"others":[49],"remain":[50],"valid.":[51],"Neural":[52],"contextual":[53],"bandits":[54],"accumulate":[55],"confidence":[56,95],"monotonically":[57],"via":[58],"the":[59,82,133,140,163],"Gram":[60,83],"matrix,":[61],"with":[62,132,176,219],"no":[63,113],"mechanism":[64],"to":[65,190],"direct":[66],"uncertainty":[67],"where":[68,88],"predictions":[69,89],"have":[70],"gone":[71],"wrong.":[72],"We":[73,98],"propose":[74],"PEARL":[75,156],"(Prediction":[76],"Error-Adaptive":[77],"Regularized":[78],"Learning),":[79],"reverses":[81],"update":[84],"along":[85],"feature":[86],"directions":[87],"surprisingly":[91],"wrong,":[92],"leaving":[94],"intact":[96],"elsewhere.":[97],"prove":[99],"that":[100,194,207],"(i)":[101],"any":[102,220],"non-adaptive":[103],"policy":[104,202],"suffers":[105],"linear":[106],"regret":[107,119,130],"after":[108],"a":[109,146,204],"shift,":[111],"(ii)":[112],"fixed":[114],"exploration":[115],"rate":[116],"achieves":[117],"low":[118],"in":[120,209],"both":[121],"stationary":[122],"regimes,":[125],"(iii)":[127],"PEARL's":[128],"cumulative":[129],"scales":[131],"number":[134],"of":[135],"shifted":[136],"users":[137],"N_s,":[138],"not":[139],"total":[141],"population":[142],"N.":[143],"On":[144],"PRISM,":[145],"selection":[150,201],"benchmark":[151],"(1,500":[152],"users,":[153],"21":[154],"LLMs),":[155],"beats":[157,183],"greedy":[158,177],"by":[159,168,188],"+4.0":[160],"points":[161,170],"best":[164],"neural":[165],"baseline":[167],"+2.5":[169],"(p<0.001),":[171],"performing":[173],"on":[174,211],"par":[175],"when":[178],"stable.":[181],"It":[182],"sliding-window":[184],"discounted":[186],"approaches":[187],"3":[189],"10":[191],"points,":[192],"showing":[193],"selective":[195],"forgetting":[196],"outperforms":[197],"uniform":[198],"forgetting.":[199],"The":[200],"is":[203],"~100K-parameter":[205],"MLP":[206],"runs":[208],"milliseconds":[210],"CPU,":[212],"leaves":[213],"weights":[215],"frozen,":[216],"works":[218],"API-based":[221],"model.":[222]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-11-06T00:00:00"}