{"id":"https://openalex.org/W3123553048","doi":"https://doi.org/10.1109/tit.2022.3169440","title":"A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization","display_name":"A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization","publication_year":2022,"publication_date":"2022-04-21","ids":{"openalex":"https://openalex.org/W3123553048","doi":"https://doi.org/10.1109/tit.2022.3169440","mag":"3123553048"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2022.3169440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2022.3169440","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"article","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/A5064082810","display_name":"Tyler Sypherd","orcid":"https://orcid.org/0000-0003-2327-3485"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tyler Sypherd","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0003-2327-3485","affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064392614","display_name":"Mario D\u00edaz","orcid":"https://orcid.org/0000-0002-9321-9815"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mario Diaz","raw_affiliation_strings":["Instituto de Investigaciones en Matem&#x00E1;ticas Aplicadas y Sistemas, Universidad Nacional Aut&#x00F3;noma de M&#x00E9;xico, Coyoac&#x00E1;n, Mexico"],"raw_orcid":"https://orcid.org/0000-0002-9321-9815","affiliations":[{"raw_affiliation_string":"Instituto de Investigaciones en Matem&#x00E1;ticas Aplicadas y Sistemas, Universidad Nacional Aut&#x00F3;noma de M&#x00E9;xico, Coyoac&#x00E1;n, Mexico","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030453479","display_name":"John Kevin Cava","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Kevin Cava","raw_affiliation_strings":["School of Computing and Augmented Intelligence (SCAI), Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Augmented Intelligence (SCAI), Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068042294","display_name":"Gautam Dasarathy","orcid":"https://orcid.org/0000-0003-2252-2988"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gautam Dasarathy","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0003-2252-2988","affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064699160","display_name":"Peter Kairouz","orcid":"https://orcid.org/0000-0001-6897-5937"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Kairouz","raw_affiliation_strings":["Google, Menlo Park, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Menlo Park, CA, USA","institution_ids":["https://openalex.org/I1291425158","https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065366998","display_name":"Lalitha Sankar","orcid":"https://orcid.org/0000-0001-8122-5444"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lalitha Sankar","raw_affiliation_strings":["School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA"],"raw_orcid":"https://orcid.org/0000-0001-8122-5444","affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":1.9835,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.84074115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"68","issue":"9","first_page":"6021","last_page":"6051"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.996999979019165,"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":0.9966999888420105,"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/notation","display_name":"Notation","score":0.6871578097343445},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5225971937179565},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4035673141479492},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.37101513147354126},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.13748914003372192}],"concepts":[{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.6871578097343445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5225971937179565},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4035673141479492},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.37101513147354126},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.13748914003372192}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tit.2022.3169440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2022.3169440","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1381962724","display_name":"Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching","funder_award_id":"2003111","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2559213121","display_name":"CIF: Small: Collaborative Research: Generative Adversarial Privacy: A Data-driven Approach to Guaranteeing Privacy and Utility","funder_award_id":"1815361","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G320740471","display_name":"Unifying Information- and Optimization-Theoretic Approaches for Modeling and Training Generative Adversarial Networks","funder_award_id":"2134256","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4326984630","display_name":"RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19","funder_award_id":"2031799","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5131973795","display_name":"CIF: Small: Alpha Loss: A New Framework for Understanding and Trading Off Computation, Accuracy, and Robustness in Machine Learning","funder_award_id":"2007688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6091963581","display_name":null,"funder_award_id":"N00014-21-1-2615","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G8493427052","display_name":"CIF: Medium: Collaborative Research: Information-theoretic Guarantees on Privacy in the Age of Learning","funder_award_id":"1901243","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":109,"referenced_works":["https://openalex.org/W607505555","https://openalex.org/W836608889","https://openalex.org/W1480376833","https://openalex.org/W1480538416","https://openalex.org/W1522301498","https://openalex.org/W1899372466","https://openalex.org/W1926592634","https://openalex.org/W1964840769","https://openalex.org/W1968934255","https://openalex.org/W1982032418","https://openalex.org/W1996437515","https://openalex.org/W2022465039","https://openalex.org/W2034365297","https://openalex.org/W2041404167","https://openalex.org/W2049279961","https://openalex.org/W2089394015","https://openalex.org/W2099111195","https://openalex.org/W2101557761","https://openalex.org/W2122882636","https://openalex.org/W2134829400","https://openalex.org/W2136688338","https://openalex.org/W2147524058","https://openalex.org/W2150477941","https://openalex.org/W2151532749","https://openalex.org/W2158460069","https://openalex.org/W2296319761","https://openalex.org/W2490901831","https://openalex.org/W2594265094","https://openalex.org/W2750384547","https://openalex.org/W2752851182","https://openalex.org/W2787248994","https://openalex.org/W2789350251","https://openalex.org/W2799032899","https://openalex.org/W2805329953","https://openalex.org/W2898193427","https://openalex.org/W2910572885","https://openalex.org/W2910589063","https://openalex.org/W2913266441","https://openalex.org/W2914704901","https://openalex.org/W2942228371","https://openalex.org/W2945033152","https://openalex.org/W2946193510","https://openalex.org/W2950787360","https://openalex.org/W2952568974","https://openalex.org/W2952659618","https://openalex.org/W2955863859","https://openalex.org/W2962719046","https://openalex.org/W2962933129","https://openalex.org/W2962972504","https://openalex.org/W2963122491","https://openalex.org/W2963351448","https://openalex.org/W2963427613","https://openalex.org/W2963480765","https://openalex.org/W2964065616","https://openalex.org/W2964253222","https://openalex.org/W2970308742","https://openalex.org/W2970971581","https://openalex.org/W2975512938","https://openalex.org/W2976533594","https://openalex.org/W2976697791","https://openalex.org/W2978725006","https://openalex.org/W2979452771","https://openalex.org/W2981873476","https://openalex.org/W2998261580","https://openalex.org/W3004593944","https://openalex.org/W3012949032","https://openalex.org/W3021623424","https://openalex.org/W3033031655","https://openalex.org/W3048007023","https://openalex.org/W3080444421","https://openalex.org/W3099188570","https://openalex.org/W3100231902","https://openalex.org/W3104165014","https://openalex.org/W3123190877","https://openalex.org/W3170524081","https://openalex.org/W3199071121","https://openalex.org/W3215049250","https://openalex.org/W4213263325","https://openalex.org/W4229706427","https://openalex.org/W4236362309","https://openalex.org/W4250589301","https://openalex.org/W4287116430","https://openalex.org/W4293846201","https://openalex.org/W6623399679","https://openalex.org/W6675234955","https://openalex.org/W6680137255","https://openalex.org/W6680251445","https://openalex.org/W6683018860","https://openalex.org/W6738074204","https://openalex.org/W6743598654","https://openalex.org/W6743688258","https://openalex.org/W6747381837","https://openalex.org/W6748318183","https://openalex.org/W6750707585","https://openalex.org/W6752356114","https://openalex.org/W6759129252","https://openalex.org/W6759367437","https://openalex.org/W6762879059","https://openalex.org/W6762970624","https://openalex.org/W6763094270","https://openalex.org/W6764363478","https://openalex.org/W6766978945","https://openalex.org/W6772932235","https://openalex.org/W6773077377","https://openalex.org/W6774860519","https://openalex.org/W6779289895","https://openalex.org/W6791199311","https://openalex.org/W6800961365","https://openalex.org/W6840799836"],"related_works":["https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W4245490552","https://openalex.org/W4225152035","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W3104631496","https://openalex.org/W4288772858","https://openalex.org/W2888243295","https://openalex.org/W2012841980"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,44,71],"tunable":[3],"loss":[4,17,29],"function":[5],"called$\\alpha":[6],"$-loss,":[7],"parameterized":[8],"by$\\alpha":[9],"\\in":[10],"(0,\\infty]$,":[11],"which":[12],"interpolates":[13],"between":[14],"the":[15,21,27,35,54,82,130],"exponential":[16],"($\\alpha":[18,23,30,118,146,154],"=":[19,24,31,119],"1/2$),":[20],"log-loss":[22,117],"1$),":[25,120],"and":[26,49,69,93,121,149],"0\u20131":[28],"\\infty":[32],"$),":[33],"for":[34,129],"machine":[36],"learning":[37],"setting":[38],"of":[39],"classification.":[40],"Theoretically,":[41],"we":[42,88,125],"illustrate":[43],"fundamental":[45],"connection":[46],"between$\\alpha":[47],"$-loss":[48,57,114],"Arimoto":[50],"conditional":[51],"entropy,":[52],"verify":[53],"classification-calibration":[55],"of$\\alpha":[56,85],"in":[58,77],"order":[59,78],"to":[60,79,122,143,151],"demonstrate":[61],"asymptotic":[62],"optimality":[63],"via":[64],"Rademacher":[65],"complexity":[66],"generalization":[67],"techniques,":[68],"build-upon":[70],"notion":[72],"called":[73],"strictly":[74],"local":[75],"quasi-convexity":[76],"quantitatively":[80],"characterize":[81],"optimization":[83],"landscape":[84],"$-loss.":[86],"Practically,":[87],"perform":[89],"class":[90],"imbalance,":[91],"robustness,":[92],"classification":[94],"experiments":[95],"on":[96],"benchmark":[97],"image":[98],"datasets":[99],"using":[100],"convolutional-neural-networks.":[101],"Our":[102],"main":[103],"practical":[104],"conclusion":[105],"is":[106],"that":[107],"certain":[108],"tasks":[109],"may":[110],"benefit":[111],"from":[112,116],"tuning$\\alpha":[113],"away":[115],"this":[123],"end":[124],"provide":[126,139],"simple":[127],"heuristics":[128],"practitioner.":[131],"In":[132],"particular,":[133],"navigating":[134],"the$\\alpha":[135],"$hyperparameter":[136],"can":[137],"readily":[138],"superior":[140],"model":[141],"robustness":[142],"label":[144],"flips":[145],">":[147],"1$)":[148],"sensitivity":[150],"imbalanced":[152],"classes":[153],"<":[155],"1$).":[156]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}