{"id":"https://openalex.org/W4386072173","doi":"https://doi.org/10.1109/isit54713.2023.10206951","title":"Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem","display_name":"Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem","publication_year":2023,"publication_date":"2023-06-25","ids":{"openalex":"https://openalex.org/W4386072173","doi":"https://doi.org/10.1109/isit54713.2023.10206951"},"language":"en","primary_location":{"id":"doi:10.1109/isit54713.2023.10206951","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isit54713.2023.10206951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-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/A5029262439","display_name":"Ruida Zhou","orcid":"https://orcid.org/0000-0002-8855-2031"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruida Zhou","raw_affiliation_strings":["Texas A&M University,Department of Electrical and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675045","display_name":"Chao Tian","orcid":"https://orcid.org/0000-0001-8752-6141"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Tian","raw_affiliation_strings":["Texas A&M University,Department of Electrical and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006265534","display_name":"Tie Liu","orcid":"https://orcid.org/0000-0002-6412-2432"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tie Liu","raw_affiliation_strings":["Texas A&M University,Department of Electrical and Computer Engineering"],"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Electrical and Computer Engineering","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029262439"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":0.3491,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6507778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"903","last_page":"908"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9990000128746033,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9990000128746033,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9990000128746033,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.7231500744819641},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7146021723747253},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6297825574874878},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.5657702088356018},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49800992012023926},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4897198975086212},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4604284465312958},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4549223780632019},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4369221031665802},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3927914500236511},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3530678153038025},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.32672062516212463},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2400403916835785},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19776654243469238}],"concepts":[{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.7231500744819641},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7146021723747253},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6297825574874878},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.5657702088356018},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49800992012023926},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4897198975086212},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4604284465312958},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4549223780632019},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4369221031665802},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3927914500236511},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3530678153038025},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32672062516212463},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2400403916835785},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19776654243469238},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit54713.2023.10206951","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/isit54713.2023.10206951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W656592592","https://openalex.org/W1482235099","https://openalex.org/W1995875735","https://openalex.org/W1997834106","https://openalex.org/W2099111195","https://openalex.org/W2129069237","https://openalex.org/W2135469284","https://openalex.org/W2258658829","https://openalex.org/W2617539226","https://openalex.org/W2910589063","https://openalex.org/W2962702650","https://openalex.org/W2975622175","https://openalex.org/W3019669224","https://openalex.org/W3023926204","https://openalex.org/W3024982789","https://openalex.org/W3036167779","https://openalex.org/W3081051123","https://openalex.org/W3093959689","https://openalex.org/W3100231902","https://openalex.org/W3106002857","https://openalex.org/W3110257065","https://openalex.org/W3110364518","https://openalex.org/W3117521716","https://openalex.org/W3125313957","https://openalex.org/W3162926177","https://openalex.org/W3176801395","https://openalex.org/W3213800969","https://openalex.org/W4211049957","https://openalex.org/W4226237166","https://openalex.org/W4236362309","https://openalex.org/W4286897948","https://openalex.org/W4288028699","https://openalex.org/W4289655190","https://openalex.org/W4289655268","https://openalex.org/W4289655368","https://openalex.org/W4289655432","https://openalex.org/W4292998917","https://openalex.org/W4293774175","https://openalex.org/W4297738147","https://openalex.org/W4300167521","https://openalex.org/W4306294888","https://openalex.org/W4310902449","https://openalex.org/W4313304931","https://openalex.org/W4386072173","https://openalex.org/W6628883064","https://openalex.org/W6692563690","https://openalex.org/W6735544424","https://openalex.org/W6738074204","https://openalex.org/W6751754507","https://openalex.org/W6767717172","https://openalex.org/W6772932235","https://openalex.org/W6775928558","https://openalex.org/W6779823529","https://openalex.org/W6786375611","https://openalex.org/W6795288823","https://openalex.org/W6802995403","https://openalex.org/W6804346497","https://openalex.org/W6810814440"],"related_works":["https://openalex.org/W2132137594","https://openalex.org/W2069783012","https://openalex.org/W2085967955","https://openalex.org/W4256335355","https://openalex.org/W3098612666","https://openalex.org/W2160478240","https://openalex.org/W2138867060","https://openalex.org/W2023859141","https://openalex.org/W2182290077","https://openalex.org/W1499116563"],"abstract_inverted_index":{"We":[0,173],"provide":[1],"a":[2,139,143,148,167,202],"new":[3,93,109],"information-theoretic":[4,31,81],"generalization":[5,32,86,129,170],"error":[6,33,130,171],"bound":[7,94,135,188,204],"that":[8,176,205],"is":[9,44,136,159,206],"exactly":[10,189],"tight":[11,209],"(i.e.,":[12],"matching":[13],"even":[14],"the":[15,18,47,50,77,85,96,115,121,128,134,152,156,178,182,187,197],"constant)":[16],"for":[17,88],"canonical":[19],"quadratic":[20],"Gaussian":[21,54],"mean":[22,51],"estimation":[23],"problem.":[24],"Despite":[25],"considerable":[26],"existing":[27,64],"efforts":[28],"in":[29,69,83,138,155,210],"deriving":[30],"bounds,":[34],"applying":[35,114],"them":[36,192],"to":[37,127,151,201],"this":[38,70,211],"simple":[39],"setting":[40],"where":[41],"sample":[42],"average":[43],"used":[45],"as":[46],"estimate":[48],"of":[49,53,80,113,117,163],"value":[52],"data":[55],"has":[56,73,106],"not":[57,194],"yielded":[58],"satisfying":[59],"results.":[60],"In":[61],"fact,":[62],"most":[63],"bounds":[65,82],"are":[66],"order-wise":[67],"loose":[68],"setting,":[71,158],"which":[72,146,199],"raised":[74],"concerns":[75],"about":[76],"fundamental":[78],"capability":[79],"reasoning":[84],"behavior":[87],"machine":[89],"learning.":[90],"The":[91,161],"proposed":[92,99],"adopts":[95],"individual-sample-based":[97],"approach":[98],"by":[100],"Bu":[101],"et":[102],"al.,":[103],"but":[104],"also":[105,207],"several":[107],"key":[108],"ingredients.":[110],"Firstly,":[111],"instead":[112],"change":[116],"measure":[118],"inequality":[119],"on":[120],"loss":[122],"function,":[123],"we":[124],"apply":[125],"it":[126],"function":[131],"itself;":[132],"secondly,":[133],"derived":[137],"conditional":[140,179],"manner;":[141],"lastly,":[142],"reference":[144,183],"distribution,":[145],"bears":[147],"certain":[149],"similarity":[150],"prior":[153],"distribution":[154,184],"Bayesian":[157],"introduced.":[160],"combination":[162],"these":[164],"components":[165],"produces":[166],"general":[168],"KL-divergence-based":[169],"bound.":[172],"further":[174],"show":[175],"although":[177],"bounding":[180],"and":[181],"can":[185],"make":[186],"tight,":[190],"removing":[191],"does":[193],"significantly":[195],"degrade":[196],"bound,":[198],"leads":[200],"mutual-information-based":[203],"asymptotically":[208],"setting.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}