{"id":"https://openalex.org/W4288043559","doi":"https://doi.org/10.48550/arxiv.2207.10673","title":"Correcting Model Bias with Sparse Implicit Processes","display_name":"Correcting Model Bias with Sparse Implicit Processes","publication_year":2022,"publication_date":"2022-07-21","ids":{"openalex":"https://openalex.org/W4288043559","doi":"https://doi.org/10.48550/arxiv.2207.10673"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.10673","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10673","pdf_url":"https://arxiv.org/pdf/2207.10673","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2207.10673","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049461768","display_name":"Sim\u00f3n Rodr\u00edguez Santana","orcid":"https://orcid.org/0000-0003-3760-0520"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Santana, Sim\u00f3n Rodr\u00edguez","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059760234","display_name":"Luis A. Ortega Andr\u00e9s","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ortega, Luis A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003318791","display_name":"Daniel Hern\u00e1ndez-Lobato","orcid":"https://orcid.org/0000-0001-5845-437X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hern\u00e1ndez-Lobato, Daniel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055345742","display_name":"Bryan Zald\u00edvar","orcid":"https://orcid.org/0000-0002-6313-6525"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zald\u00edvar, Bryan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5049461768"],"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9980999827384949,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9980999827384949,"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":0.9919000267982483,"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":0.9711999893188477,"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/computer-science","display_name":"Computer science","score":0.7342036366462708},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6620537042617798},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6080021262168884},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5694098472595215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5444614887237549},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5355387330055237},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5346450209617615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5207793116569519},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4767504632472992},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4395659863948822},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.43028318881988525},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.39393749833106995},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3740377128124237},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17773222923278809}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7342036366462708},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6620537042617798},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6080021262168884},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5694098472595215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5444614887237549},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5355387330055237},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5346450209617615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5207793116569519},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4767504632472992},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4395659863948822},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43028318881988525},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.39393749833106995},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3740377128124237},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17773222923278809},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2207.10673","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10673","pdf_url":"https://arxiv.org/pdf/2207.10673","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2207.10673","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2207.10673","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2207.10673","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2207.10673","pdf_url":"https://arxiv.org/pdf/2207.10673","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3159703654","https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2010643158","https://openalex.org/W3049691116","https://openalex.org/W2106867672","https://openalex.org/W3081214562","https://openalex.org/W4310268968","https://openalex.org/W2950975704","https://openalex.org/W4297815943"],"abstract_inverted_index":{"Model":[0,15],"selection":[1],"in":[2,55,77],"machine":[3],"learning":[4,13],"(ML)":[5],"is":[6,74,82,100,128],"a":[7],"crucial":[8],"part":[9],"of":[10,30,52,64,69,102,130,143],"the":[11,22,28,41,93,107,114,118,136,140,144],"Bayesian":[12,34,48],"procedure.":[14],"choice":[16],"may":[17],"impose":[18],"strong":[19],"biases":[20],"on":[21,92],"resulting":[23],"predictions,":[24],"which":[25],"can":[26],"hinder":[27],"performance":[29],"methods":[31],"such":[32],"as":[33],"neural":[35,38],"networks":[36],"and":[37,85],"samplers.":[39],"On":[40],"other":[42],"hand,":[43],"newly":[44],"proposed":[45],"approaches":[46],"for":[47],"ML":[49],"exploit":[50],"features":[51],"approximate":[53],"inference":[54],"function":[56],"space":[57],"with":[58],"implicit":[59],"stochastic":[60],"processes":[61],"(a":[62],"generalization":[63],"Gaussian":[65],"processes).":[66],"The":[67],"approach":[68],"Sparse":[70],"Implicit":[71],"Processes":[72],"(SIP)":[73],"particularly":[75],"successful":[76],"this":[78],"regard,":[79],"since":[80],"it":[81],"fully":[83],"trainable":[84],"achieves":[86],"flexible":[87],"predictions.":[88],"Here,":[89],"we":[90],"expand":[91],"original":[94],"experiments":[95],"to":[96,124],"show":[97,125],"that":[98,126,134],"SIP":[99,127],"capable":[101,129],"correcting":[103],"model":[104],"bias":[105],"when":[106],"data":[108,137],"generating":[109],"mechanism":[110],"differs":[111],"strongly":[112],"from":[113],"one":[115],"implied":[116],"by":[117],"model.":[119,149],"We":[120],"use":[121],"synthetic":[122],"datasets":[123],"providing":[131],"predictive":[132],"distributions":[133],"reflect":[135],"better":[138],"than":[139],"exact":[141],"predictions":[142],"initial,":[145],"but":[146],"wrongly":[147],"assumed":[148]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}