{"id":"https://openalex.org/W7161582808","doi":"https://doi.org/10.48550/arxiv.2605.15675","title":"Interaction-Aware Influence Functions for Group Attribution","display_name":"Interaction-Aware Influence Functions for Group Attribution","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161582808","doi":"https://doi.org/10.48550/arxiv.2605.15675"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15675","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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.15675","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128704307","display_name":"Jaeseung Heo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heo, Jaeseung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136413511","display_name":"Kyeongheung Yun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun, Kyeongheung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102504434","display_name":"Youngbin Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Youngbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136441196","display_name":"Sehyun Hwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Sehyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136402401","display_name":"Jungseul Ok","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ok, Jungseul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136426947","display_name":"Dongwoo Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Dongwoo","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/T12535","display_name":"Machine Learning and Data Classification","score":0.3504999876022339,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.3504999876022339,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3285999894142151,"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/T10028","display_name":"Topic Modeling","score":0.07259999960660934,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7135000228881836},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6092000007629395},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5691999793052673},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.424699991941452},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.3061999976634979}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7135000228881836},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6092000007629395},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5691999793052673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5408999919891357},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4629000127315521},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3237000107765198},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.29030001163482666},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2648000121116638},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.259799987077713}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15675","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.15675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15675","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":"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"},"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":{"Influence":[0],"functions":[1],"approximate":[2],"how":[3,49,79],"removing":[4],"a":[5,9,19,27,55,107,158,181],"training":[6],"example":[7],"changes":[8],"quantity":[10],"of":[11,26,29,40,57,176],"interest,":[12],"called":[13],"the":[14,24,31,37,53,65,84,88,94,103,113,120],"target":[15,89],"function,":[16],"such":[17],"as":[18,157],"held-out":[20],"loss.":[21],"To":[22],"estimate":[23],"influence":[25,75,83,150],"group":[28],"examples,":[30],"standard":[32,104,184],"practice":[33],"is":[34],"to":[35,90],"sum":[36,45,66,105],"individual":[38],"influences":[39],"its":[41],"members.":[42],"However,":[43],"this":[44],"does":[46],"not":[47],"capture":[48],"examples":[50,58,82],"jointly":[51],"affect":[52],"target:":[54],"pair":[56],"may":[59],"be":[60],"redundant":[61],"or":[62],"complementary,":[63],"but":[64],"cannot":[67],"distinguish":[68],"these":[69],"cases.":[70],"We":[71,122],"propose":[72],"an":[73,99],"interaction-aware":[74],"function":[76],"that":[77,101,111],"characterizes":[78],"interactions":[80],"between":[81,115],"target.":[85,121],"By":[86],"expanding":[87],"second":[91],"order":[92],"around":[93],"trained":[95],"parameters,":[96],"we":[97],"obtain":[98],"estimator":[100,126,142],"augments":[102],"with":[106],"pairwise":[108],"interaction":[109],"term":[110],"captures":[112],"alignment":[114],"two":[116,128],"examples'":[117],"effects":[118],"on":[119,131,165,174],"empirically":[123],"evaluate":[124],"our":[125,141],"in":[127,180],"settings.":[129,153],"First,":[130],"six":[132],"dataset-model":[133],"pairs":[134],"spanning":[135],"logistic":[136],"regression,":[137],"MLPs,":[138],"and":[139,171],"ResNet-9,":[140],"tracks":[143],"leave-group-out":[144],"retraining":[145],"substantially":[146],"better":[147],"than":[148],"first-order":[149],"across":[151],"all":[152],"Second,":[154],"when":[155],"used":[156],"greedy":[159],"selection":[160,186],"rule":[161],"for":[162],"instruction-tuning":[163],"data":[164],"Llama-3.1-8B,":[166],"it":[167],"beats":[168],"prior":[169],"influence-based":[170,185],"representation-similarity":[172],"baselines":[173],"five":[175],"seven":[177],"downstream":[178],"tasks,":[179],"regime":[182],"where":[183],"underperforms":[187],"random":[188],"selection.":[189]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-19T00:00:00"}