{"id":"https://openalex.org/W3106574079","doi":null,"title":"Supercharging Imbalanced Data Learning With Causal Representation Transfer","display_name":"Supercharging Imbalanced Data Learning With Causal Representation Transfer","publication_year":2020,"publication_date":"2020-11-25","ids":{"openalex":"https://openalex.org/W3106574079","mag":"3106574079"},"language":"en","primary_location":{"id":"mag:3106574079","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2011.12454","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2011.12454","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061180609","display_name":"Junya Chen","orcid":"https://orcid.org/0000-0002-3431-1857","observed_orcids":["https://orcid.org/0000-0002-3431-1857"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junya Chen","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3431-1857","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029371083","display_name":"Zidi Xiu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zidi Xiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067727757","display_name":"Benjamin Alan Goldstein","orcid":"https://orcid.org/0000-0001-5261-3632","observed_orcids":["https://orcid.org/0000-0001-5261-3632"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Benjamin A. Goldstein","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5261-3632","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056639842","display_name":"Ricardo Henao","orcid":"https://orcid.org/0000-0003-4980-845X","observed_orcids":["https://orcid.org/0000-0003-4980-845X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ricardo Henao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016448581","display_name":"Lawrence Carin","orcid":"https://orcid.org/0000-0001-6277-7948","observed_orcids":["https://orcid.org/0000-0001-6277-7948"]},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Carin","raw_affiliation_strings":["Duke University#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-6277-7948","affiliations":[{"raw_affiliation_string":"Duke University#TAB#","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071582087","display_name":"Chenyang Tao","orcid":"https://orcid.org/0000-0002-2155-8180","observed_orcids":["https://orcid.org/0000-0002-2155-8180"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chenyang Tao","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2155-8180","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.8539999723434448,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.8539999723434448,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.05130000039935112,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.05119999870657921,"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/class-imbalance","display_name":"class imbalance","score":0.9710000157356262},{"id":"https://openalex.org/keywords/long-tailed-learning","display_name":"long-tailed learning","score":0.875},{"id":"https://openalex.org/keywords/minority-class-representation","display_name":"minority class representation","score":0.6349999904632568},{"id":"https://openalex.org/keywords/invariance-principle","display_name":"invariance principle","score":0.652999997138977},{"id":"https://openalex.org/keywords/sample-efficiency","display_name":"sample efficiency","score":0.6809999942779541},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.6579999923706055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65510094165802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5793164968490601},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5545861124992371},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5517117381095886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5486288666725159},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5105289220809937},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5085958242416382},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5078840851783752},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4728700816631317},{"id":"https://openalex.org/C75917345","wikidata":"https://www.wikidata.org/wiki/Q2725298","display_name":"Sampling bias","level":3,"score":0.4455057382583618},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4407021999359131},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.4287516474723816},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4272604286670685},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.42477452754974365},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42127394676208496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.365384578704834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18413600325584412},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.16785231232643127},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11278751492500305},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3106574079","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2011.12454","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3106574079","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2011.12454","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[{"score":0.7578999996185303,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W169052826","https://openalex.org/W1915968771","https://openalex.org/W2055755416","https://openalex.org/W2074399002","https://openalex.org/W2099741732","https://openalex.org/W2104933073","https://openalex.org/W2123649031","https://openalex.org/W2148143831","https://openalex.org/W2163490846","https://openalex.org/W2163848058","https://openalex.org/W2183341477","https://openalex.org/W2188333210","https://openalex.org/W2284188655","https://openalex.org/W2604763608","https://openalex.org/W2686124727","https://openalex.org/W2753300133","https://openalex.org/W2892246301","https://openalex.org/W2906251086","https://openalex.org/W2924282518","https://openalex.org/W2949736877","https://openalex.org/W2952120674","https://openalex.org/W2954996726","https://openalex.org/W2956607501","https://openalex.org/W2962933664","https://openalex.org/W2963207607","https://openalex.org/W2963264829","https://openalex.org/W2963341924","https://openalex.org/W2963351448","https://openalex.org/W2963453204","https://openalex.org/W2963743626","https://openalex.org/W2963755523","https://openalex.org/W2964193753","https://openalex.org/W2981748719","https://openalex.org/W3005304986","https://openalex.org/W3007233055","https://openalex.org/W3025509622","https://openalex.org/W3034942609","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W3167126744","https://openalex.org/W7044273779","https://openalex.org/W4403681397","https://openalex.org/W2952120674","https://openalex.org/W3034711780","https://openalex.org/W3096688134","https://openalex.org/W2995450817","https://openalex.org/W4390673948","https://openalex.org/W4281752577","https://openalex.org/W4385695913"],"abstract_inverted_index":{"Dealing":[0],"with":[1,165],"severe":[2],"class":[3],"imbalance":[4],"poses":[5],"a":[6,35,87,131],"major":[7],"challenge":[8],"for":[9,39],"real-world":[10,172],"applications,":[11],"especially":[12,38],"when":[13],"the":[14,55,79,91,98,110,119,138,148],"accurate":[15],"classification":[16,151],"and":[17,74,171],"generalization":[18,76],"of":[19,23,82,140,160,169],"minority":[20,141],"classes":[21,112],"is":[22,34,95,145,163],"primary":[24],"interest.":[25],"In":[26],"computer":[27,173],"vision,":[28],"learning":[29],"from":[30,109],"long":[31],"tailed":[32],"datasets":[33],"recurring":[36],"theme,":[37],"natural":[40],"image":[41],"datasets.":[42],"While":[43],"existing":[44,149],"solutions":[45],"mostly":[46],"appeal":[47],"to":[48,53,62,70,113,129,136,147],"sampling":[49],"or":[50,58],"weighting":[51],"adjustments":[52],"alleviate":[54],"pathological":[56],"imbalance,":[57],"imposing":[59],"inductive":[60],"bias":[61],"prioritize":[63],"non-spurious":[64],"associations,":[65],"we":[66],"take":[67],"novel":[68],"perspectives":[69],"promote":[71],"sample":[72],"efficiency":[73],"model":[75],"based":[77],"on":[78],"invariance":[80],"principles":[81],"causality.":[83],"Our":[84,143],"proposal":[85,162],"posits":[86],"meta-distributional":[88],"scenario,":[89],"where":[90],"data":[92,133],"generating":[93],"mechanism":[94],"invariant":[96],"across":[97],"label-conditional":[99],"feature":[100,121],"distributions.":[101],"Such":[102],"causal":[103,132],"assumption":[104],"enables":[105],"efficient":[106],"knowledge":[107],"transfer":[108],"dominant":[111],"their":[114],"under-represented":[115],"counterparts,":[116],"even":[117],"if":[118],"respective":[120],"distributions":[122],"show":[123],"apparent":[124],"disparities.":[125],"This":[126],"allows":[127],"us":[128],"leverage":[130],"inflation":[134],"procedure":[135],"enlarge":[137],"representation":[139],"classes.":[142],"development":[144],"orthogonal":[146],"extreme":[150],"techniques":[152],"thus":[153],"can":[154],"be":[155],"seamlessly":[156],"integrated.":[157],"The":[158],"utility":[159],"our":[161],"validated":[164],"an":[166],"extensive":[167],"set":[168],"synthetic":[170],"vision":[174],"tasks":[175],"against":[176],"SOTA":[177],"solutions.":[178]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2020-12-07T00:00:00"}
