{"id":"https://openalex.org/W2021197309","doi":"https://doi.org/10.1145/2783258.2783416","title":"SAME but Different","display_name":"SAME but Different","publication_year":2015,"publication_date":"2015-08-07","ids":{"openalex":"https://openalex.org/W2021197309","doi":"https://doi.org/10.1145/2783258.2783416","mag":"2021197309"},"language":"en","primary_location":{"id":"doi:10.1145/2783258.2783416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2783258.2783416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5046520437","display_name":"Huasha Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huasha Zhao","raw_affiliation_strings":["University of California Berkeley, Berkeley, CA, USA","University of California, Berkeley, Berkeley, CA USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060745724","display_name":"Biye Jiang","orcid":"https://orcid.org/0009-0001-5814-1581"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Biye Jiang","raw_affiliation_strings":["University of California Berkeley, Berkeley, CA, USA","University of California, Berkeley, Berkeley, CA USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089723214","display_name":"John Canny","orcid":"https://orcid.org/0000-0002-7161-7927"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John F. Canny","raw_affiliation_strings":["University of California Berkeley, Berkeley, CA, USA","University of California, Berkeley, Berkeley, CA USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039683044","display_name":"Bobby Jaros","orcid":null},"institutions":[{"id":"https://openalex.org/I1325784139","display_name":"Yahoo (United Kingdom)","ror":"https://ror.org/038p3gq39","country_code":"GB","type":"company","lineage":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"]},{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Bobby Jaros","raw_affiliation_strings":["Yahoo Inc, Sunnyvale, CA, USA","Yahoo, Inc., Sunnyvale, CA, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Inc, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"Yahoo, Inc., Sunnyvale, CA, USA#TAB#","institution_ids":["https://openalex.org/I1325784139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0864,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.93115849,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1495","last_page":"1502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9990000128746033,"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/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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9965000152587891,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9959999918937683,"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.7459198236465454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7139540314674377},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6041154265403748},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5560344457626343},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5056676864624023},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4617502987384796},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.44390904903411865},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4351259469985962},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.428943395614624},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.41896989941596985},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4141542613506317},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3931811451911926},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3492175340652466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2625318765640259},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24563154578208923},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22872179746627808},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.08273178339004517}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7459198236465454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7139540314674377},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6041154265403748},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5560344457626343},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5056676864624023},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4617502987384796},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.44390904903411865},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4351259469985962},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.428943395614624},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.41896989941596985},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4141542613506317},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3931811451911926},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3492175340652466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2625318765640259},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24563154578208923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22872179746627808},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.08273178339004517},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"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/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C182310444","wikidata":"https://www.wikidata.org/wiki/Q1332643","display_name":"Boundary value problem","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2783258.2783416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2783258.2783416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","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":20,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W1511187233","https://openalex.org/W1516111018","https://openalex.org/W1880262756","https://openalex.org/W1934021597","https://openalex.org/W1999702719","https://openalex.org/W2001082470","https://openalex.org/W2020999234","https://openalex.org/W2049633694","https://openalex.org/W2052261215","https://openalex.org/W2072634211","https://openalex.org/W2113651538","https://openalex.org/W2131979817","https://openalex.org/W2138996412","https://openalex.org/W2145702984","https://openalex.org/W2156358825","https://openalex.org/W2165599843","https://openalex.org/W2175160295","https://openalex.org/W2255383145","https://openalex.org/W4245883374"],"related_works":["https://openalex.org/W2072169887","https://openalex.org/W4386272753","https://openalex.org/W2343819364","https://openalex.org/W2133205540","https://openalex.org/W2086004744","https://openalex.org/W2005354445","https://openalex.org/W2064483411","https://openalex.org/W2796920963","https://openalex.org/W2891616219","https://openalex.org/W2017516907"],"abstract_inverted_index":{"Gibbs":[0,48],"sampling":[1,101],"is":[2,18,34,104,180],"a":[3],"workhorse":[4],"for":[5,14,29,64,108],"Bayesian":[6],"inference":[7,24,124],"but":[8,147],"has":[9],"several":[10],"limitations":[11],"when":[12],"used":[13],"parameter":[15,39,44,58],"estimation,":[16],"and":[17,60,86,103,170,184],"often":[19,68],"much":[20],"slower":[21,88],"than":[22,174],"non-sampling":[23],"methods.":[25],"SAME":[26,50,94,120,132],"(State":[27],"Augmentation":[28],"Marginal":[30],"Estimation)":[31],"[15,":[32],"8]":[33],"an":[35,105],"approach":[36],"to":[37,121,162,182,188],"MAP":[38,66],"estimation":[40],"which":[41],"gives":[42,139],"improved":[43],"estimates":[45],"over":[46],"direct":[47],"sampling.":[49],"can":[51],"be":[52,186],"viewed":[53],"as":[54],"cooling":[55],"the":[56,65,79,91,97,100,117,143,163],"posterior":[57],"distribution":[59],"allows":[61],"annealed":[62],"search":[63],"parameters,":[67],"yielding":[69],"very":[70],"high":[71],"quality":[72],"estimates.":[73],"But":[74],"it":[75],"does":[76],"so":[77],"at":[78],"expense":[80],"of":[81,119],"additional":[82],"samples":[83],"per":[84],"iteration":[85],"generally":[87],"performance.":[89],"On":[90],"other":[92,175,190],"hand,":[93],"dramatically":[95],"increases":[96],"parallelism":[98],"in":[99],"schedule,":[102],"excellent":[106],"match":[107],"modern":[109,126],"(SIMD)":[110],"hardware.":[111,127],"In":[112],"this":[113],"paper":[114],"we":[115],"explore":[116],"application":[118],"graphical":[122],"model":[123],"on":[125,155],"We":[128,152],"show":[129],"that":[130],"combining":[131],"with":[133,142,148],"factored":[134],"sample":[135],"representation":[136],"(or":[137],"approximation)":[138],"throughput":[140],"competitive":[141],"fastest":[144,164],"symbolic":[145],"methods,":[146],"potentially":[149],"better":[150],"quality.":[151],"describe":[153],"experiments":[154],"Latent":[156],"Dirichlet":[157],"Allocation,":[158],"achieving":[159],"speeds":[160],"similar":[161],"reported":[165],"methods":[166],"(online":[167],"Variational":[168],"Bayes)":[169],"lower":[171],"cross-validated":[172],"loss":[173],"LDA":[176],"implementations.":[177],"The":[178],"method":[179],"simple":[181],"implement":[183],"should":[185],"applicable":[187],"many":[189],"models.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2016-06-24T00:00:00"}
