{"id":"https://openalex.org/W2185386567","doi":"https://doi.org/10.3929/ethz-b-000333042","title":"Sampling from Probabilistic Submodular Models","display_name":"Sampling from Probabilistic Submodular Models","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2185386567","doi":"https://doi.org/10.3929/ethz-b-000333042","mag":"2185386567"},"language":"en","primary_location":{"id":"pmh:oai:www.research-collection.ethz.ch:20.500.11850/333042","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/333042","pdf_url":"http://hdl.handle.net/20.500.11850/333042","source":{"id":"https://openalex.org/S4306402302","display_name":"Repository for Publications and Research Data (ETH Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I35440088","host_organization_name":"ETH Zurich","host_organization_lineage":["https://openalex.org/I35440088"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11850/333042","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005116939","display_name":"Alkis Gotovos","orcid":"https://orcid.org/0000-0002-3902-8890"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Gotovos, Alkis","raw_affiliation_strings":["ETH Z\u00fcrich"],"affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5005116939"],"corresponding_institution_ids":["https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":1.6797,"has_fulltext":true,"cited_by_count":27,"citation_normalized_percentile":{"value":0.84876104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":100},"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9991999864578247,"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"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9976999759674072,"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":0.9968000054359436,"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/submodular-set-function","display_name":"Submodular set function","score":0.9806797504425049},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5913040637969971},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5518449544906616},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5508520603179932},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.542974054813385},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5185707211494446},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4584289789199829},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4529817998409271},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.427140474319458},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3871355950832367},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.2819277346134186},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.18815407156944275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15487849712371826},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13346099853515625}],"concepts":[{"id":"https://openalex.org/C178621042","wikidata":"https://www.wikidata.org/wiki/Q7631710","display_name":"Submodular set function","level":2,"score":0.9806797504425049},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5913040637969971},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5518449544906616},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5508520603179932},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.542974054813385},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5185707211494446},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4584289789199829},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4529817998409271},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.427140474319458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3871355950832367},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.2819277346134186},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.18815407156944275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15487849712371826},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13346099853515625},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:www.research-collection.ethz.ch:20.500.11850/333042","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/333042","pdf_url":"http://hdl.handle.net/20.500.11850/333042","source":{"id":"https://openalex.org/S4306402302","display_name":"Repository for Publications and Research Data (ETH Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I35440088","host_organization_name":"ETH Zurich","host_organization_lineage":["https://openalex.org/I35440088"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.3929/ethz-b-000333042","is_oa":true,"landing_page_url":"https://doi.org/10.3929/ethz-b-000333042","pdf_url":null,"source":{"id":"https://openalex.org/S7407051236","display_name":"ETH Z\u00fcrich Research Collection","issn_l":null,"issn":[],"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"}],"best_oa_location":{"id":"pmh:oai:www.research-collection.ethz.ch:20.500.11850/333042","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11850/333042","pdf_url":"http://hdl.handle.net/20.500.11850/333042","source":{"id":"https://openalex.org/S4306402302","display_name":"Repository for Publications and Research Data (ETH Zurich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I35440088","host_organization_name":"ETH Zurich","host_organization_lineage":["https://openalex.org/I35440088"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2185386567.pdf","grobid_xml":"https://content.openalex.org/works/W2185386567.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W318698866","https://openalex.org/W634962210","https://openalex.org/W1511986666","https://openalex.org/W1576820654","https://openalex.org/W1600293573","https://openalex.org/W1680189815","https://openalex.org/W1986979529","https://openalex.org/W1990872838","https://openalex.org/W2011193572","https://openalex.org/W2024710200","https://openalex.org/W2033281046","https://openalex.org/W2043694962","https://openalex.org/W2061820396","https://openalex.org/W2106285343","https://openalex.org/W2109260999","https://openalex.org/W2140422174","https://openalex.org/W2144933361","https://openalex.org/W2145825677","https://openalex.org/W2147716762","https://openalex.org/W2148134592","https://openalex.org/W2157316274","https://openalex.org/W2161611531","https://openalex.org/W2168256531","https://openalex.org/W2260080129","https://openalex.org/W2625347919","https://openalex.org/W2962795549","https://openalex.org/W2963968545","https://openalex.org/W3102205987","https://openalex.org/W3103014337","https://openalex.org/W3118655244"],"related_works":["https://openalex.org/W2147716762","https://openalex.org/W585006759","https://openalex.org/W2008095352","https://openalex.org/W3103014337","https://openalex.org/W2963597340","https://openalex.org/W2549152635","https://openalex.org/W2187580259","https://openalex.org/W2115857089","https://openalex.org/W2006672084","https://openalex.org/W1600293573","https://openalex.org/W1680189815","https://openalex.org/W2964279659","https://openalex.org/W2347557623","https://openalex.org/W2307132432","https://openalex.org/W2148516480","https://openalex.org/W2024710200","https://openalex.org/W1897230935","https://openalex.org/W1605829949","https://openalex.org/W1915921069","https://openalex.org/W2340475112"],"abstract_inverted_index":{"Submodular":[0],"and":[1,16,26,66,76,82,105,117,126,149],"supermodular":[2],"functions":[3,33],"have":[4,21],"found":[5],"wide":[6],"applicability":[7],"in":[8,102],"machine":[9],"learning,":[10],"capturing":[11],"notions":[12,20,42],"such":[13,147],"as":[14],"diversity":[15],"regularity,":[17],"respectively.":[18],"These":[19],"deep":[22],"consequences":[23],"for":[24],"optimization,":[25,40],"the":[27,71,90,122,127,137,140],"problem":[28],"of":[29,74,92,139,146],"(approximately)":[30],"optimizing":[31],"submodular":[32],"has":[34],"received":[35],"much":[36,80],"attention.":[37],"However,":[38],"beyond":[39],"these":[41],"allow":[43],"specifying":[44],"expressive":[45],"probabilistic":[46],"models":[47,65,78],"that":[48],"can":[49],"be":[50],"used":[51],"to":[52,98],"quantify":[53],"predictive":[54],"uncertainty":[55],"via":[56],"marginal":[57],"inference.":[58],"Prominent,":[59],"well-studied":[60],"special":[61],"cases":[62],"include":[63],"Ising":[64],"determinantal":[67],"point":[68],"processes,":[69],"but":[70],"general":[72,103],"class":[73],"log-submodular":[75,104],"log-supermodular":[77,106],"is":[79],"richer":[81],"little":[83],"studied.":[84],"In":[85,108],"this":[86],"paper,":[87],"we":[88,110],"investigate":[89],"use":[91],"Markov":[93],"chain":[94],"Monte":[95],"Carlo":[96],"sampling":[97,115],"perform":[99],"approximate":[100],"inference":[101],"models.":[107],"particular,":[109],"consider":[111],"a":[112,152],"simple":[113],"Gibbs":[114,141],"procedure,":[116],"establish":[118],"two":[119],"sufficient":[120],"conditions,":[121],"first":[123],"guaranteeing":[124],"polynomial-time,":[125],"second":[128],"fast":[129],"(O(n":[130],"log":[131],"n))":[132],"mixing.":[133],"We":[134],"also":[135],"evaluate":[136],"efficiency":[138],"sampler":[142],"on":[143],"three":[144],"examples":[145],"models,":[148],"compare":[150],"against":[151],"recently":[153],"proposed":[154],"variational":[155],"approach.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":14}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}