{"id":"https://openalex.org/W2609763701","doi":"https://doi.org/10.5075/epfl-thesis-7637","title":"Applications of Approximate Learning and Inference for Probabilistic Models","display_name":"Applications of Approximate Learning and Inference for Probabilistic Models","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2609763701","doi":"https://doi.org/10.5075/epfl-thesis-7637","mag":"2609763701"},"language":"en","primary_location":{"id":"pmh:oai:infoscience.tind.io:227482","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/227482","pdf_url":"https://infoscience.epfl.ch/record/227482/files/EPFL_TH7637.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoral thesis"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://infoscience.epfl.ch/record/227482/files/EPFL_TH7637.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084773758","display_name":"Young Jun Ko","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ko, Young Jun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5084773758"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.9954000115394592,"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":0.9954000115394592,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9746999740600586,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9413999915122986,"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/computer-science","display_name":"Computer science","score":0.5679801106452942},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5570549964904785},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46170809864997864},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4599567651748657},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43418294191360474},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40585318207740784},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3728824853897095}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5679801106452942},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5570549964904785},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46170809864997864},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4599567651748657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43418294191360474},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40585318207740784},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3728824853897095}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:infoscience.tind.io:227482","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/227482","pdf_url":"https://infoscience.epfl.ch/record/227482/files/EPFL_TH7637.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoral thesis"},{"id":"doi:10.5075/epfl-thesis-7637","is_oa":true,"landing_page_url":"https://doi.org/10.5075/epfl-thesis-7637","pdf_url":null,"source":{"id":"https://openalex.org/S4306400488","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"},{"id":"mag:2609763701","is_oa":false,"landing_page_url":"https://infoscience.epfl.ch/record/227482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:infoscience.tind.io:227482","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/227482","pdf_url":"https://infoscience.epfl.ch/record/227482/files/EPFL_TH7637.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoral thesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2609763701.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2563338646","https://openalex.org/W2275207081","https://openalex.org/W2948166700","https://openalex.org/W3014266848","https://openalex.org/W2159432773","https://openalex.org/W3107609871","https://openalex.org/W2605907621","https://openalex.org/W2068176821","https://openalex.org/W304228415","https://openalex.org/W3129132991","https://openalex.org/W2247318628","https://openalex.org/W2464714129","https://openalex.org/W101939087","https://openalex.org/W2890896922","https://openalex.org/W2726321600","https://openalex.org/W2337617599","https://openalex.org/W2157544223","https://openalex.org/W3091703258","https://openalex.org/W2985663642","https://openalex.org/W3036118961"],"abstract_inverted_index":{"We":[0,323,381],"develop":[1,95,198,295,393],"approximate":[2,97,201,396],"inference":[3,79,86,98,202,363],"and":[4,58,70,116,135,270,316,392,455,486],"learning":[5],"methods":[6,296],"for":[7,52,68,100,111,118,184,204,251,302],"facilitating":[8],"the":[9,25,46,71,82,85,112,181,218,237,245,255,267,271,276,317,342,366,373,403,412,417,428,441],"use":[10,230],"of":[11,29,37,48,63,84,104,157,222,225,233,241,244,248,260,290,305,310,320,424,430,435,458],"probabilistic":[12],"modeling":[13],"techniques":[14],"motivated":[15],"by":[16,41,155,191,284,355,411],"applications":[17],"in":[18,133,141,144,166,172,353,416,440,447],"two":[19,101,265,303],"different":[20,102],"areas.":[21],"First,":[22],"we":[23,44,94,178,197,229,263,280,294,339,371,421,468],"consider":[24,45,264],"ill-posed":[26],"inverse":[27],"problem":[28,47,87],"recovering":[30],"an":[31,34,128,249,361],"image":[32,250],"from":[33,54,377,483],"underdetermined":[35],"system":[36],"linear":[38,108],"measurements":[39],"corrupted":[40],"noise.":[42],"Second,":[43],"inferring":[49],"user":[50,59,261,425,459],"preferences":[51,283],"items":[53],"counts,":[55],"pairwise":[56,321,378,413],"comparisons":[57],"activity":[60,426],"logs,":[61],"instances":[62],"implicit":[64,306,368],"feedback.":[65],"Plausible":[66],"models":[67,259],"images":[69],"noise,":[72],"incurred":[73],"when":[74],"recording":[75],"them,":[76],"render":[77],"posterior":[78],"intractable,":[80],"while":[81],"scale":[83,446],"makes":[88],"sampling":[89],"based":[90,475],"approximations":[91],"ineffective.":[92],"Therefore,":[93],"deterministic":[96],"algorithms":[99],"augmentations":[103],"a":[105,161,192,199,210,223,285,314,328,383,394,444,470],"typical":[106],"sparse":[107],"model:":[109],"first,":[110],"rectified-linear":[113,124],"Poisson":[114,125,168,334],"likelihood,":[115],"second,":[117],"tree-structured":[119,231],"super-Gaussian":[120],"mixture":[121,206,220,226],"models.":[122,227],"The":[123],"likelihood":[126],"is":[127],"alternative":[129],"noise":[130],"model,":[131,338],"applicable":[132],"astronomical":[134],"biomedical":[136],"imaging":[137],"applications,":[138,449],"that":[139,151,180,208,341,401,480],"operate":[140],"intensity":[142],"regimes":[143],"which":[145],"quantum":[146],"effects":[147],"lead":[148],"to":[149,214,235,297,350,360,407],"observations":[150],"are":[152],"best":[153],"described":[154],"counts":[156,309],"particles":[158],"arriving":[159],"at":[160,427,443],"sensor,":[162],"as":[163,165],"well":[164],"general":[167],"regression":[169,391],"problems":[170],"arising":[171],"various":[173],"fields.":[174],"In":[175,254,275,365],"this":[176,299,337],"context":[177],"show,":[179],"model-specific":[182],"computations":[183],"Expectation":[185,398],"Propagation":[186],"can":[187,346,452],"be":[188,347,351],"robustly":[189],"solved":[190],"simple":[193],"dynamic":[194,273,419],"program.":[195],"Next,":[196],"scalable":[200],"algorithm":[203,400],"structured":[205],"models,":[207],"uses":[209],"discrete":[211],"graphical":[212],"model":[213,236,324,332,386,422,474],"represent":[215,281],"dependencies":[216],"between":[217],"latent":[219,286,300,329,374,408],"components":[221],"collection":[224],"Specifically,":[228],"mixtures":[232],"super-Gaussians":[234],"persistence":[238],"across":[239],"scales":[240],"large":[242,445],"coefficients":[243],"Wavelet":[246],"transform":[247],"improved":[252],"reconstruction.":[253],"second":[256,367],"part":[257],"on":[258,476],"preference,":[262],"settings:":[266],"global":[268,277],"static":[269,278],"contextual":[272,418,456],"setting.":[274],"setting,":[279,420],"user-item":[282],"low-rank":[287,384],"matrix.":[288],"Instead":[289],"using":[291,327],"numeric":[292],"ratings":[293],"infer":[298,372],"representation":[301],"types":[304],"feedback:":[307],"aggregate":[308,436],"users":[311],"interacting":[312],"with":[313,333,387],"service":[315],"binary":[318],"outcomes":[319],"comparisons.":[322,414],"count":[325],"data":[326],"Gaussian":[330,344],"bilinear":[331,385],"likelihoods.":[335],"For":[336],"show":[340],"Variational":[343],"approximation":[345],"further":[348],"relaxed":[349],"available":[352],"closed-form":[354],"adding":[356],"additional":[357],"constraints,":[358],"leading":[359],"efficient":[362],"algorithm.":[364],"feedback":[369],"scenario,":[370],"preference":[375,379],"matrix":[376],"statements.":[380],"combine":[382],"non-parameteric":[388],"item-":[389],"feature":[390],"novel":[395],"variational":[397],"Maximization":[399],"mitigates":[402],"computational":[404],"challenges":[405],"due":[406],"couplings":[409],"induced":[410],"Finally,":[415],"sequences":[423,451],"granularity":[429],"single":[431],"interaction":[432],"events":[433],"instead":[434],"counts.":[437],"Routinely":[438],"gathered":[439],"background":[442],"many":[448],"such":[450,466],"reveal":[453],"temporal":[454],"aspects":[457],"behavior":[460],"through":[461],"recurrent":[462,477],"patterns.":[463],"To":[464],"describe":[465],"data,":[467],"propose":[469],"generic":[471],"collaborative":[472,484],"sequence":[473],"neural":[478],"networks,":[479],"combines":[481],"ideas":[482],"filtering":[485],"language":[487],"modeling.":[488]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
