{"id":"https://openalex.org/W2127163205","doi":"https://doi.org/10.1145/1102351.1102412","title":"PAC-Bayes risk bounds for sample-compressed Gibbs classifiers","display_name":"PAC-Bayes risk bounds for sample-compressed Gibbs classifiers","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2127163205","doi":"https://doi.org/10.1145/1102351.1102412","mag":"2127163205"},"language":"en","primary_location":{"id":"doi:10.1145/1102351.1102412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning  - ICML '05","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/A5054812796","display_name":"Fran\u00e7ois Laviolette","orcid":"https://orcid.org/0000-0002-1937-2512"},"institutions":[{"id":"https://openalex.org/I43406934","display_name":"Universit\u00e9 Laval","ror":"https://ror.org/04sjchr03","country_code":"CA","type":"education","lineage":["https://openalex.org/I43406934"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Fran\u00e7ois Laviolette","raw_affiliation_strings":["Universit\u00e9 Laval, Qu\u00e9bec (Canada)"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Laval, Qu\u00e9bec (Canada)","institution_ids":["https://openalex.org/I43406934"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034069136","display_name":"Mario Marchand","orcid":"https://orcid.org/0000-0002-7078-7393"},"institutions":[{"id":"https://openalex.org/I43406934","display_name":"Universit\u00e9 Laval","ror":"https://ror.org/04sjchr03","country_code":"CA","type":"education","lineage":["https://openalex.org/I43406934"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mario Marchand","raw_affiliation_strings":["Universit\u00e9 Laval, Qu\u00e9bec (Canada)"],"affiliations":[{"raw_affiliation_string":"Universit\u00e9 Laval, Qu\u00e9bec (Canada)","institution_ids":["https://openalex.org/I43406934"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5054812796"],"corresponding_institution_ids":["https://openalex.org/I43406934"],"apc_list":null,"apc_paid":null,"fwci":2.2835,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89804103,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"481","last_page":"488"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","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/T12072","display_name":"Machine Learning and Algorithms","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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.6602342128753662},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.608565628528595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5865515470504761},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5745066404342651},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5309844613075256},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4518645405769348},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38863298296928406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3670088052749634},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2977961301803589},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.06892037391662598},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.03933662176132202}],"concepts":[{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.6602342128753662},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.608565628528595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5865515470504761},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5745066404342651},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5309844613075256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4518645405769348},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38863298296928406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3670088052749634},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2977961301803589},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.06892037391662598},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.03933662176132202},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1102351.1102412","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1102351.1102412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd international conference on Machine learning  - ICML '05","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.126.8857","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.126.8857","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ift.ulaval.ca/~mmarchand/publications/pacbayesfinal_icml05.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.81.4175","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.4175","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2005/papers/061_PACBayes_LavioletteMachand.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W65382156","https://openalex.org/W1590293155","https://openalex.org/W1607832978","https://openalex.org/W1628829797","https://openalex.org/W2014384147","https://openalex.org/W2098641446","https://openalex.org/W2099111195","https://openalex.org/W2116314036","https://openalex.org/W2150434953","https://openalex.org/W2168022998","https://openalex.org/W2170207925","https://openalex.org/W2170708028"],"related_works":["https://openalex.org/W4205958290","https://openalex.org/W1470425429","https://openalex.org/W3107602296","https://openalex.org/W3014815208","https://openalex.org/W3044272884","https://openalex.org/W4306742369","https://openalex.org/W2171665309","https://openalex.org/W2086147528","https://openalex.org/W4313444831","https://openalex.org/W2949661619"],"abstract_inverted_index":{"We":[0],"extend":[1],"the":[2,6,30,38,69],"PAC-Bayes":[3,76],"theorem":[4,77],"to":[5,42],"sample-compression":[7],"setting":[8],"where":[9,61],"each":[10,62],"classifier":[11,66,82],"is":[12,49,72],"represented":[13],"by":[14,51],"two":[15],"independent":[16],"sources":[17],"of":[18,25,29,37,59],"information:":[19],"a":[20,26,34,44,53,56,65,80,85,92],"compression":[21],"set":[22,58],"which":[23],"consists":[24],"small":[27],"subset":[28],"training":[31,70],"data,":[32],"and":[33],"message":[35],"string":[36],"additional":[39],"information":[40],"needed":[41],"obtain":[43],"classifier.":[45,101],"The":[46,74],"new":[47,75],"bound":[48,95],"obtained":[50],"using":[52],"prior":[54],"over":[55,87],"data-independent":[57],"objects":[60],"object":[63],"gives":[64],"only":[67],"when":[68],"data":[71],"provided.":[73],"states":[78],"that":[79],"Gibbs":[81],"defined":[83],"on":[84],"posterior":[86],"samplecompressed":[88,100],"classifiers":[89],"can":[90],"have":[91],"smaller":[93],"risk":[94],"than":[96],"any":[97],"such":[98],"(deterministic)":[99],"1.":[102]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}