{"id":"https://openalex.org/W4401864148","doi":"https://doi.org/10.1145/3637528.3671818","title":"Efficient Exploration of the Rashomon Set of Rule-Set Models","display_name":"Efficient Exploration of the Rashomon Set of Rule-Set Models","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401864148","doi":"https://doi.org/10.1145/3637528.3671818"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671818","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671818","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671818","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671818","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054507372","display_name":"Martino Ciaperoni","orcid":"https://orcid.org/0009-0009-7581-2031"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Martino Ciaperoni","raw_affiliation_strings":["Aalto University, Espoo, Uusimaa, Finland"],"affiliations":[{"raw_affiliation_string":"Aalto University, Espoo, Uusimaa, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107250326","display_name":"Han Xiao","orcid":"https://orcid.org/0000-0003-3529-083X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han Xiao","raw_affiliation_strings":["The Upright Project, Helsinki, Uusimaa, Finland"],"affiliations":[{"raw_affiliation_string":"The Upright Project, Helsinki, Uusimaa, Finland","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022164041","display_name":"Aristides Gionis","orcid":"https://orcid.org/0000-0002-5211-112X"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Aristides Gionis","raw_affiliation_strings":["KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5054507372"],"corresponding_institution_ids":["https://openalex.org/I9927081"],"apc_list":null,"apc_paid":null,"fwci":1.0425,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.80661011,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"478","last_page":"489"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9897000193595886,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9897000193595886,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9847999811172485,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.983299970626831,"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/set","display_name":"Set (abstract data type)","score":0.7282605171203613},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.633440375328064},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.10833412408828735}],"concepts":[{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.7282605171203613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.633440375328064},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.10833412408828735}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3671818","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671818","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671818","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/131232","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/7b959f42-36c8-4f0a-90e2-c23e5b9e07d9","pdf_url":null,"source":{"id":"https://openalex.org/S4306401663","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671818","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671818","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671818","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401864148.pdf"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1606825810","https://openalex.org/W2015731569","https://openalex.org/W2100960835","https://openalex.org/W2116666691","https://openalex.org/W2149902097","https://openalex.org/W2157501275","https://openalex.org/W2367397349","https://openalex.org/W2744365997","https://openalex.org/W2787960070","https://openalex.org/W2888390390","https://openalex.org/W2962793900","https://openalex.org/W2969476445","https://openalex.org/W3003728721","https://openalex.org/W3005073185","https://openalex.org/W3036583521","https://openalex.org/W3101981467","https://openalex.org/W3104727718","https://openalex.org/W3124373176","https://openalex.org/W3126673261","https://openalex.org/W3137125108","https://openalex.org/W3210632597","https://openalex.org/W4283168572","https://openalex.org/W4296412277","https://openalex.org/W4318822850","https://openalex.org/W4401864148","https://openalex.org/W6843020732","https://openalex.org/W6926090900","https://openalex.org/W6945111357"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Today,":[0],"as":[1],"increasingly":[2],"complex":[3],"predictive":[4],"models":[5,52,132],"are":[6],"developed,":[7],"simple":[8],"rule":[9,27],"sets":[10],"remain":[11],"a":[12,25,30,34,77,96,148],"crucial":[13],"tool":[14],"to":[15,88,125],"obtain":[16],"interpretable":[17,41],"predictions":[18],"and":[19,95],"drive":[20],"high-stakes":[21],"decision":[22],"making.":[23],"However,":[24],"single":[26],"set":[28,49,68,108,129],"provides":[29],"partial":[31],"representation":[32],"of":[33,50,65,72,102,105,130,143,150],"learning":[35,43],"task.":[36,80],"An":[37],"emerging":[38],"paradigm":[39],"in":[40,147],"machine":[42],"aims":[44],"at":[45],"exploring":[46],"the":[47,66,82,103,106,120,127,141,144],"Rashomon":[48,67,107,128],"all":[51],"exhibiting":[53],"near-optimal":[54],"performance.":[55],"Existing":[56],"work":[57],"on":[58,62],"Rashomon-set":[59],"exploration":[60],"focuses":[61],"exhaustive":[63,85,136],"search":[64],"for":[69,111,119],"particular":[70],"classes":[71],"models,":[73],"which":[74],"can":[75],"be":[76],"computationally":[78],"challenging":[79],"On":[81],"other":[83],"hand,":[84],"enumeration":[86],"leads":[87],"redundancy":[89],"that":[90],"often":[91],"is":[92,109],"not":[93],"necessary,":[94],"representative":[97],"sample":[98],"or":[99,134],"an":[100],"estimate":[101],"size":[104],"sufficient":[110],"many":[112],"applications.":[113],"In":[114],"this":[115],"work,":[116],"we":[117],"propose,":[118],"first":[121],"time,":[122],"efficient":[123],"methods":[124,146],"explore":[126],"rule-set":[131],"with":[133],"without":[135],"search.":[137],"Extensive":[138],"experiments":[139],"demonstrate":[140],"effectiveness":[142],"proposed":[145],"variety":[149],"scenarios.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}