{"id":"https://openalex.org/W7138014365","doi":"https://doi.org/10.1609/aaai.v40i26.39375","title":"Jump-teaching: Combating Sample Selection Bias via Temporal Disagreement","display_name":"Jump-teaching: Combating Sample Selection Bias via Temporal Disagreement","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138014365","doi":"https://doi.org/10.1609/aaai.v40i26.39375"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i26.39375","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39375","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39375/43336","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39375/43336","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114192465","display_name":"Kangye Ji","orcid":"https://orcid.org/0009-0007-2763-3114"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangye Ji","raw_affiliation_strings":["Xidian University\nTsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University\nTsinghua University","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129746155","display_name":"Fei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Cheng","raw_affiliation_strings":["Xidian University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087792272","display_name":"Zeqing Wang","orcid":"https://orcid.org/0000-0003-1133-7488"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]},{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["CN","SG"],"is_corresponding":false,"raw_author_name":"Zeqing Wang","raw_affiliation_strings":["Xidian University\nNational University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University\nNational University of Singapore","institution_ids":["https://openalex.org/I149594827","https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102997845","display_name":"Qichang Zhang","orcid":"https://orcid.org/0000-0002-1753-7918"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qichang Zhang","raw_affiliation_strings":["Xidian University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090537930","display_name":"Bohu Huang","orcid":"https://orcid.org/0009-0005-0978-482X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bohu Huang","raw_affiliation_strings":["Xidian University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19533993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"26","first_page":"22191","last_page":"22199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9189000129699707,"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":0.9189000129699707,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.02500000037252903,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.010400000028312206,"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/selection","display_name":"Selection (genetic algorithm)","score":0.7289000153541565},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6412000060081482},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5697000026702881},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.5103999972343445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4975000023841858},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4544999897480011},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.41990000009536743}],"concepts":[{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7289000153541565},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6412000060081482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6225000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5806000232696533},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.5103999972343445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39660000801086426},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.36059999465942383},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.259799987077713}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i26.39375","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39375","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39375/43336","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i26.39375","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i26.39375","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/39375/43336","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138014365.pdf","grobid_xml":"https://content.openalex.org/works/W7138014365.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sample":[0],"selection":[1,27,53,61,90,109,124,146],"is":[2],"a":[3,65,69,81,107,117,122,143],"straightforward":[4],"technique":[5],"to":[6,11,39,86,154],"combat":[7],"noisy":[8],"labels,":[9],"aiming":[10],"prevent":[12],"mislabeled":[13],"samples":[14],"from":[15],"degrading":[16],"the":[17,97,113],"robustness":[18],"of":[19,89,116],"neural":[20,70],"networks.":[21],"However,":[22],"existing":[23],"methods":[24],"mitigate":[25],"compounding":[26],"bias":[28,91],"either":[29],"by":[30,92,152,161],"leveraging":[31],"dual-network":[32],"disagreement":[33,74],"or":[34,101,130],"additional":[35],"forward":[36],"propagations,":[37],"leading":[38],"multiplied":[40],"training":[41,77,150],"overhead.":[42],"To":[43],"address":[44],"this":[45],"challenge,":[46],"we":[47,105],"introduce":[48],"Jump-teaching,":[49],"an":[50],"efficient":[51],"sample":[52],"framework":[54],"for":[55,99,121],"debiased":[56],"model":[57,83],"update":[58,84],"and":[59,156],"simplified":[60],"criterion.":[62],"Based":[63],"on":[64,112,127],"key":[66],"observation":[67],"that":[68,136],"network":[71],"exhibits":[72],"significant":[73],"across":[75],"different":[76],"iterations,":[78],"Jump-teaching":[79,137],"proposes":[80],"jump-manner":[82],"strategy":[85],"enable":[87],"self-correction":[88],"harnessing":[93],"temporal":[94],"disagreement,":[95],"eliminating":[96],"need":[98],"multi-network":[100],"multi-round":[102],"training.":[103],"Furthermore,":[104],"employ":[106],"sample-wise":[108],"criterion":[110],"building":[111],"intra":[114],"variance":[115],"decomposed":[118],"single":[119],"loss":[120],"fine-grained":[123],"without":[125],"relying":[126],"batch-wise":[128],"ranking":[129],"dataset-wise":[131],"modeling.":[132],"Extensive":[133],"experiments":[134],"demonstrate":[135],"outperforms":[138],"state-of-the-art":[139],"counterparts":[140],"while":[141],"achieving":[142],"nearly":[144],"overhead-free":[145],"procedure,":[147],"which":[148],"boosts":[149],"speed":[151],"up":[153],"4.47\u00d7":[155],"reduces":[157],"peak":[158],"memory":[159],"footprint":[160],"54%.":[162]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-18T00:00:00"}
