{"id":"https://openalex.org/W2188523697","doi":null,"title":"Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions","display_name":"Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions","publication_year":2012,"publication_date":"2012-06-16","ids":{"openalex":"https://openalex.org/W2188523697","mag":"2188523697"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.297.1403","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.1403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.tufts.edu/~roni/PUB/colt12-oam.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5020490663","display_name":"Yuyang Wang","orcid":"https://orcid.org/0009-0005-4492-1594","observed_orcids":["https://orcid.org/0009-0005-4492-1594","https://orcid.org/0000-0002-0139-879X"]},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuyang Wang","raw_affiliation_strings":["Tufts University, Medford, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tufts University, Medford, United States","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013618541","display_name":"Roni Khardon","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I121934306","display_name":"Tufts University","ror":"https://ror.org/05wvpxv85","country_code":"US","type":"education","lineage":["https://openalex.org/I121934306"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Roni Khardon","raw_affiliation_strings":["Tufts University, Medford, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tufts University, Medford, United States","institution_ids":["https://openalex.org/I121934306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062813136","display_name":"Dmitry Pechyony","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I10734018","display_name":"Akamai (United States)","ror":"https://ror.org/03tarb191","country_code":"US","type":"company","lineage":["https://openalex.org/I10734018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitry Pechyony","raw_affiliation_strings":["Akamai (United States), Cambridge, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Akamai (United States), Cambridge, United States","institution_ids":["https://openalex.org/I10734018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000992993","display_name":"Rosie Jones","orcid":"https://orcid.org/0009-0000-3821-1207","observed_orcids":["https://orcid.org/0009-0000-3821-1207","https://orcid.org/0000-0001-5689-2632"]},"institutions":[{"id":"https://openalex.org/I10734018","display_name":"Akamai (United States)","ror":"https://ror.org/03tarb191","country_code":"US","type":"company","lineage":["https://openalex.org/I10734018"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rosie Jones","raw_affiliation_strings":["Akamai (United States), Cambridge, United States"],"raw_orcid":"https://orcid.org/0009-0000-3821-1207","affiliations":[{"raw_affiliation_string":"Akamai (United States), Cambridge, United States","institution_ids":["https://openalex.org/I10734018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3254,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.8825413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.8823000192642212,"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":0.8823000192642212,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.06689999997615814,"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":0.02239999920129776,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/e-learning","display_name":"online learning","score":0.7910000085830688},{"id":"https://openalex.org/keywords/generalization-bounds","display_name":"generalization bounds","score":0.9959999918937683},{"id":"https://openalex.org/keywords/risk-bounds","display_name":"risk bounds","score":0.6159999966621399},{"id":"https://openalex.org/keywords/data-dependent-bounds","display_name":"data-dependent bounds","score":0.7049999833106995},{"id":"https://openalex.org/keywords/perceptron-algorithm","display_name":"perceptron algorithm","score":0.6439999938011169},{"id":"https://openalex.org/keywords/bipartite-ranking","display_name":"bipartite ranking","score":0.9750000238418579}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7929613590240479},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.7043668627738953},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5932335257530212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5915877819061279},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.5467953681945801},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5284338593482971},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5251604914665222},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.46326783299446106},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4583749771118164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4342867434024811},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3859112858772278},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38418108224868774},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35969290137290955},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1223154366016388},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.297.1403","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.1403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.tufts.edu/~roni/PUB/colt12-oam.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.297.1599","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.1599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.tufts.edu/~roni/Posters/colt2012-poster-auc.pdf","raw_type":"text"},{"id":"mag:2188523697","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v23/wang12/wang12.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306418075","display_name":"Conference on Learning Theory","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Conference on Learning Theory","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W16571744","https://openalex.org/W93885277","https://openalex.org/W1481525066","https://openalex.org/W1502981577","https://openalex.org/W1534694010","https://openalex.org/W1540586255","https://openalex.org/W1543818864","https://openalex.org/W1545509459","https://openalex.org/W1564947197","https://openalex.org/W1978810924","https://openalex.org/W1979711143","https://openalex.org/W2046176422","https://openalex.org/W2047221353","https://openalex.org/W2047278710","https://openalex.org/W2065861851","https://openalex.org/W2101723727","https://openalex.org/W2107890099","https://openalex.org/W2109339818","https://openalex.org/W2111233787","https://openalex.org/W2118075434","https://openalex.org/W2121038136","https://openalex.org/W2121824931","https://openalex.org/W2138682935","https://openalex.org/W2144064004","https://openalex.org/W2148825261"],"related_works":["https://openalex.org/W7096606896","https://openalex.org/W1578240932","https://openalex.org/W2149433471","https://openalex.org/W2607502190","https://openalex.org/W7095157749","https://openalex.org/W2513039057","https://openalex.org/W2773481315","https://openalex.org/W2808413970","https://openalex.org/W2274052023","https://openalex.org/W3212345415"],"abstract_inverted_index":{"Efficient":[0],"online":[1,36,54,101],"learning":[2,14,37],"with":[3,39,56],"pairwise":[4,40,66,144],"loss":[5,41,59],"functions":[6],"is":[7],"a":[8,57,115,126,139,151],"crucial":[9],"component":[10],"in":[11,80,103],"building":[12],"largescale":[13],"system":[15],"that":[16,45],"maximizes":[17],"the":[18,21,32,46,69,77,81,89,93,120,130,134,142,156],"area":[19],"under":[20],"Receiver":[22],"Operator":[23],"Characteristic":[24],"(ROC)":[25],"curve.":[26],"In":[27,122],"this":[28],"paper":[29],"we":[30,84,124,149],"investigate":[31],"generalization":[33,51],"performance":[34],"of":[35,53,92,95,105,129,155],"algorithms":[38,55],"functions.":[42],"We":[43],"show":[44,111],"existing":[47],"proof":[48,74],"techniques":[49,75],"for":[50,76,88,133],"bounds":[52,79,87],"pointwise":[58],"can":[60],"not":[61],"be":[62],"directly":[63],"applied":[64],"to":[65,113],"losses.":[67],"Using":[68],"Hoeffding-Azuma":[70],"inequality":[71],"and":[72,110],"various":[73],"risk":[78,91,117,153],"batch":[82],"learning,":[83],"derive":[85],"data-dependent":[86],"average":[90],"sequence":[94],"hypotheses":[96],"generated":[97],"by":[98],"an":[99,106],"arbitrary":[100],"learner":[102],"terms":[104],"easily":[107],"computable":[108],"statistic,":[109],"how":[112],"extract":[114],"low":[116],"hypothesis":[118],"from":[119],"sequence.":[121],"addition,":[123],"analyze":[125],"natural":[127],"extension":[128],"perceptron":[131],"algorithm":[132],"bipartite":[135],"ranking":[136],"problem":[137],"providing":[138],"bound":[140],"on":[141],"empirical":[143],"loss.":[145],"Combining":[146],"these":[147],"results":[148],"get":[150],"complete":[152],"analysis":[154],"proposed":[157],"algorithm.":[158]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2016-06-24T00:00:00"}