{"id":"https://openalex.org/W3029990846","doi":"https://doi.org/10.1145/3318464.3380604","title":"Learning to Validate the Predictions of Black Box Classifiers on Unseen Data","display_name":"Learning to Validate the Predictions of Black Box Classifiers on Unseen Data","publication_year":2020,"publication_date":"2020-05-29","ids":{"openalex":"https://openalex.org/W3029990846","doi":"https://doi.org/10.1145/3318464.3380604","mag":"3029990846"},"language":"en","primary_location":{"id":"doi:10.1145/3318464.3380604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318464.3380604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090934117","display_name":"Sebastian Schelter","orcid":"https://orcid.org/0000-0003-4722-5840"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sebastian Schelter","raw_affiliation_strings":["New York University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027386823","display_name":"Tammo Rukat","orcid":"https://orcid.org/0000-0002-6186-0077"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tammo Rukat","raw_affiliation_strings":["Amazon Research, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon Research, Berlin, Germany","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024953386","display_name":"Felix Bie\u00dfmann","orcid":"https://orcid.org/0000-0002-3422-1026"},"institutions":[{"id":"https://openalex.org/I129643931","display_name":"Berliner Hochschule f\u00fcr Technik","ror":"https://ror.org/00w7whj55","country_code":"DE","type":"education","lineage":["https://openalex.org/I129643931"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Felix Biessmann","raw_affiliation_strings":["Beuth University Berlin, Berlin, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beuth University Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I129643931"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7599,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.92375619,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1289","last_page":"1299"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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":0.9987999796867371,"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":0.9969000220298767,"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/black-box","display_name":"Black box","score":0.7785353660583496},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7677956223487854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6526272296905518},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6029708385467529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5769051313400269},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5753545761108398},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5017328262329102},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.46132057905197144},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.42127302289009094},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3019500970840454},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.100248783826828},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07538637518882751}],"concepts":[{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.7785353660583496},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7677956223487854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6526272296905518},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6029708385467529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5769051313400269},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5753545761108398},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5017328262329102},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.46132057905197144},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.42127302289009094},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3019500970840454},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.100248783826828},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07538637518882751},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3318464.3380604","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318464.3380604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","raw_type":"proceedings-article"},{"id":"pmh:oai:dare.uva.nl:openaire_cris_publications/90369541-5331-4835-850f-69146c0b0d43","is_oa":false,"landing_page_url":"https://handle.uba.uva.nl/personal/pure/en/publications/learning-to-validate-the-predictions-of-black-box-classifiers-on-unseen-data(90369541-5331-4835-850f-69146c0b0d43).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400088","display_name":"UvA-DARE (University of Amsterdam)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887064364","host_organization_name":"University of Amsterdam","host_organization_lineage":["https://openalex.org/I887064364"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Schelter, S, Rukat, T & Biessmann, F 2020, Learning to Validate the Predictions of Black Box Classifiers on Unseen Data. in SIGMOD '20 : proceedings of the 2020 ACM SIGMOD International Conference on Management of Data : June 14-19, 2020, Portland, OR, USA. Association for Computing Machinery, New York, NY, pp. 1289-1299, 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020, Portland, United States, 14/06/20. https://doi.org/10.1145/3318464.3380604","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1485587488","https://openalex.org/W1493730910","https://openalex.org/W1991410152","https://openalex.org/W2101234009","https://openalex.org/W2112483442","https://openalex.org/W2153929442","https://openalex.org/W2162651021","https://openalex.org/W2182361439","https://openalex.org/W2189162242","https://openalex.org/W2295598076","https://openalex.org/W2338065342","https://openalex.org/W2342249984","https://openalex.org/W2357449897","https://openalex.org/W2616028256","https://openalex.org/W2889249015","https://openalex.org/W2895242880","https://openalex.org/W2903158431","https://openalex.org/W2905588001","https://openalex.org/W2926314329","https://openalex.org/W2943955885","https://openalex.org/W2946595616","https://openalex.org/W2962739340","https://openalex.org/W2971192944","https://openalex.org/W2971274354","https://openalex.org/W3007501395","https://openalex.org/W3102476541","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W1980614089","https://openalex.org/W3029990846","https://openalex.org/W4285322112","https://openalex.org/W3158596343","https://openalex.org/W4292794239","https://openalex.org/W4385572030"],"abstract_inverted_index":{"Machine":[0],"Learning":[1],"(ML)":[2],"models":[3],"are":[4,46,67],"difficult":[5],"to":[6,23,69],"maintain":[7],"in":[8,54],"production":[9],"settings.":[10],"In":[11],"particular,":[12],"deviations":[13],"of":[14,63],"the":[15,27,32,55,60],"unseen":[16],"serving":[17,56],"data":[18,29,57],"(for":[19],"which":[20,31],"we":[21],"want":[22],"compute":[24],"predictions)":[25],"from":[26],"source":[28],"(on":[30],"model":[33,42],"was":[34],"trained)":[35],"pose":[36],"a":[37,64],"central":[38],"challenge,":[39],"especially":[40],"when":[41],"training":[43],"and":[44],"prediction":[45],"outsourced":[47],"via":[48],"cloud":[49],"services.":[50],"Errors":[51],"or":[52],"shifts":[53],"can":[58],"affect":[59],"predictive":[61],"quality":[62],"model,":[65],"but":[66],"hard":[68],"detect":[70],"for":[71],"engineers":[72],"operating":[73],"ML":[74],"deployments.":[75]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
