{"id":"https://openalex.org/W3135316635","doi":"https://doi.org/10.48550/arxiv.2102.13640","title":"NOMU: Neural Optimization-based Model Uncertainty","display_name":"NOMU: Neural Optimization-based Model Uncertainty","publication_year":2021,"publication_date":"2021-02-26","ids":{"openalex":"https://openalex.org/W3135316635","doi":"https://doi.org/10.48550/arxiv.2102.13640","mag":"3135316635"},"language":"en","primary_location":{"id":"pmh:oai:www.zora.uzh.ch:232046","doi":null,"is_oa":true,"landing_page_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","pdf_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Heiss, Jakob M; Weissteiner, Jakob; Wutte, Hanna S; Seuken, Sven; Teichmann, Josef (2022). NOMU: Neural Optimization-based Model Uncertainty. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), Baltimore, United States of America, 17 July 2022 - 23 July 2022. PMLR, 8708-8758.","raw_type":"info:eu-repo/semantics/conferenceObject"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010858034","display_name":"Jakob Heiss","orcid":"https://orcid.org/0000-0003-1447-6782","observed_orcids":["https://orcid.org/0000-0003-1447-6782"]},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Heiss, Jakob","raw_affiliation_strings":["ETH Z\u00fcrich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089458452","display_name":"Jakob Weissteiner","orcid":"https://orcid.org/0009-0007-6431-0226","observed_orcids":["https://orcid.org/0009-0007-6431-0226"]},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Weissteiner, Jakob","raw_affiliation_strings":["University of zurich;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of zurich;","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036552778","display_name":"Hanna Wutte","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Wutte, Hanna","raw_affiliation_strings":["ETH Z\u00fcrich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045325634","display_name":"Sven Seuken","orcid":"https://orcid.org/0000-0001-8525-8120","observed_orcids":["https://orcid.org/0000-0001-8525-8120"]},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Seuken, Sven","raw_affiliation_strings":["University of zurich;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of zurich;","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084818994","display_name":"Josef Teichmann","orcid":"https://orcid.org/0000-0003-0125-7484","observed_orcids":["https://orcid.org/0000-0003-0125-7484"]},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Teichmann, Josef","raw_affiliation_strings":["ETH Z\u00fcrich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2222,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.8219531,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"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":0.7348999977111816,"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":0.7348999977111816,"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":0.21969999372959137,"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/T10320","display_name":"Neural Networks and Applications","score":0.007699999958276749,"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/model-uncertainty","display_name":"model uncertainty","score":0.8320000171661377},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9440000057220459},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"regression analysis","score":0.7960000038146973},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.9129999876022339},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.746999979019165},{"id":"https://openalex.org/keywords/uncertainty-estimation","display_name":"uncertainty estimation","score":0.5109999775886536},{"id":"https://openalex.org/keywords/modular-network-architecture","display_name":"modular network architecture","score":0.7670000195503235},{"id":"https://openalex.org/keywords/loss-function-design","display_name":"loss function design","score":0.8960000276565552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.792843222618103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6090937256813049},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5755586624145508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5743817090988159},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.566935658454895},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5170543789863586},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5028137564659119},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.4766959846019745},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.41206085681915283},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09383046627044678},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"pmh:oai:www.zora.uzh.ch:232046","doi":null,"is_oa":true,"landing_page_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","pdf_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Heiss, Jakob M; Weissteiner, Jakob; Wutte, Hanna S; Seuken, Sven; Teichmann, Josef (2022). NOMU: Neural Optimization-based Model Uncertainty. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), Baltimore, United States of America, 17 July 2022 - 23 July 2022. PMLR, 8708-8758.","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:arXiv.org:2102.13640","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.13640","pdf_url":"https://arxiv.org/pdf/2102.13640","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":null,"raw_type":"text"},{"id":"pmh:oai:www.zora.uzh.ch:20.500.14742/206554","doi":null,"is_oa":true,"landing_page_url":"https://www.zora.uzh.ch/handle/20.500.14742/206554","pdf_url":null,"source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Heiss, J. M., Weissteiner, J., Wutte, H. S., Seuken, S., & Teichmann, J. (2022). NOMU: Neural Optimization-based Model Uncertainty. 8708\u20138758. https://arxiv.org/abs/2102.13640","raw_type":"Conference or Workshop Item"},{"id":"doi:10.48550/arxiv.2102.13640","doi":"https://doi.org/10.48550/arxiv.2102.13640","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2102.13640","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5167/uzh-232046","doi":"https://doi.org/10.5167/uzh-232046","is_oa":true,"landing_page_url":"https://doi.org/10.5167/uzh-232046","pdf_url":null,"source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:www.zora.uzh.ch:232046","doi":null,"is_oa":true,"landing_page_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","pdf_url":"https://www.zora.uzh.ch/id/eprint/232046/1/2102.13641.pdf","source":{"id":"https://openalex.org/S4306401281","display_name":"Zurich Open Repository and Archive (University of Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I202697423","host_organization_name":"University of Zurich","host_organization_lineage":["https://openalex.org/I202697423"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Heiss, Jakob M; Weissteiner, Jakob; Wutte, Hanna S; Seuken, Sven; Teichmann, Josef (2022). NOMU: Neural Optimization-based Model Uncertainty. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), Baltimore, United States of America, 17 July 2022 - 23 July 2022. PMLR, 8708-8758.","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3135316635.pdf","grobid_xml":"https://content.openalex.org/works/W3135316635.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W61851564","https://openalex.org/W582134693","https://openalex.org/W1560021816","https://openalex.org/W1719489212","https://openalex.org/W1746819321","https://openalex.org/W1980264541","https://openalex.org/W2096649264","https://openalex.org/W2097998348","https://openalex.org/W2099471712","https://openalex.org/W2108677974","https://openalex.org/W2132477882","https://openalex.org/W2156102187","https://openalex.org/W2164411961","https://openalex.org/W2462906003","https://openalex.org/W2529996553","https://openalex.org/W2558748708","https://openalex.org/W2560321925","https://openalex.org/W2600383743","https://openalex.org/W2788433952","https://openalex.org/W2803103814","https://openalex.org/W2810469995","https://openalex.org/W2873705236","https://openalex.org/W2901746906","https://openalex.org/W2919115771","https://openalex.org/W2948194985","https://openalex.org/W2948210138","https://openalex.org/W2951266961","https://openalex.org/W2951665052","https://openalex.org/W2952625664","https://openalex.org/W2955023002","https://openalex.org/W2960107581","https://openalex.org/W2963215553","https://openalex.org/W2986469991","https://openalex.org/W2992525328","https://openalex.org/W2994797252","https://openalex.org/W2995464762","https://openalex.org/W2995969761","https://openalex.org/W2996184071","https://openalex.org/W3034669169","https://openalex.org/W3036729765","https://openalex.org/W3037759848","https://openalex.org/W3093141676","https://openalex.org/W3186948595","https://openalex.org/W4293387938","https://openalex.org/W4382239462"],"related_works":["https://openalex.org/W3189962635","https://openalex.org/W3217106048","https://openalex.org/W4402883542","https://openalex.org/W2786643646","https://openalex.org/W3035124332","https://openalex.org/W2907425548","https://openalex.org/W3035922477","https://openalex.org/W4296177824","https://openalex.org/W4399991993","https://openalex.org/W4307232305"],"abstract_inverted_index":{"We":[0,30,155],"study":[1],"methods":[2],"for":[3,7,74,78,105,110,142],"estimating":[4],"model":[5,18,36,76,106,111,140],"uncertainty":[6,37,77,141],"neural":[8],"networks":[9],"(NNs)":[10],"in":[11,158],"regression.":[12],"To":[13,66],"isolate":[14],"the":[15],"effect":[16],"of":[17,55,91,100],"uncertainty,":[19,112],"we":[20,44,69,81],"focus":[21],"on":[22],"a":[23,71,96,118],"noiseless":[24,163],"setting":[25],"with":[26,167],"scarce":[27],"training":[28,153],"data.":[29,154],"introduce":[31,70],"five":[32,130],"important":[33],"desiderata":[34],"regarding":[35],"that":[38,46,60,126],"any":[39,143],"method":[40],"should":[41],"satisfy.":[42],"However,":[43],"find":[45],"established":[47],"benchmarks":[48],"often":[49],"fail":[50],"to":[51,94,114,133,151],"reliably":[52],"capture":[53],"some":[54],"these":[56],"desiderata,":[57],"even":[58,184],"those":[59],"are":[61],"required":[62],"by":[63],"Bayesian":[64,164],"theory.":[65],"address":[67],"this,":[68],"new":[72],"approach":[73],"capturing":[75],"NNs,":[79],"which":[80],"call":[82],"Neural":[83],"Optimization-based":[84],"Model":[85],"Uncertainty":[86],"(NOMU).":[87],"The":[88],"main":[89],"idea":[90],"NOMU":[92,127,137,157,172,183],"is":[93],"design":[95,124],"network":[97],"architecture":[98],"consisting":[99],"two":[101],"connected":[102],"sub-NNs,":[103],"one":[104,109],"prediction":[107],"and":[108,113,162],"train":[115],"it":[116],"using":[117],"carefully-designed":[119],"loss":[120],"function.":[121],"Importantly,":[122],"our":[123,129],"enforces":[125],"satisfies":[128],"desiderata.":[131],"Due":[132],"its":[134,152],"modular":[135],"architecture,":[136],"can":[138],"provide":[139],"given":[144,149],"(previously":[145],"trained)":[146],"NN":[147],"if":[148],"access":[150],"evaluate":[156],"various":[159],"regressions":[160],"tasks":[161],"optimization":[165],"(BO)":[166],"costly":[168],"evaluations.":[169],"In":[170,181],"regression,":[171],"performs":[173],"at":[174],"least":[175],"as":[176,178],"well":[177],"state-of-the-art":[179],"methods.":[180],"BO,":[182],"outperforms":[185],"all":[186],"considered":[187],"benchmarks.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-03-15T00:00:00"}