{"id":"https://openalex.org/W2982313009","doi":"https://doi.org/10.1109/wcnc.2019.8885526","title":"Determinantal thinning of point processes with network learning applications","display_name":"Determinantal thinning of point processes with network learning applications","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2982313009","doi":"https://doi.org/10.1109/wcnc.2019.8885526","mag":"2982313009"},"language":"en","primary_location":{"id":"doi:10.1109/wcnc.2019.8885526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2019.8885526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.08672","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028666798","display_name":"Bart\u0142omiej B\u0142aszczyszyn","orcid":"https://orcid.org/0000-0001-6096-4109"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I29607241","display_name":"\u00c9cole Normale Sup\u00e9rieure - PSL","ror":"https://ror.org/05a0dhs15","country_code":"FR","type":"other","lineage":["https://openalex.org/I2746051580","https://openalex.org/I29607241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"B. Blaszczyszyn","raw_affiliation_strings":["Inria/ENS, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Inria/ENS, Paris, France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I29607241"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033480448","display_name":"Paul Keeler","orcid":"https://orcid.org/0000-0002-2063-1075"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"H.P. Keeler","raw_affiliation_strings":["University of Melbourne, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9831,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.91722099,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11830","display_name":"Point processes and geometric inequalities","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T11830","display_name":"Point processes and geometric inequalities","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.9474999904632568,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9383000135421753,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.8521655797958374},{"id":"https://openalex.org/keywords/poisson-point-process","display_name":"Poisson point process","score":0.6279759407043457},{"id":"https://openalex.org/keywords/determinantal-point-process","display_name":"Determinantal point process","score":0.6225971579551697},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5068355202674866},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.4908321499824524},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.47919997572898865},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4574418067932129},{"id":"https://openalex.org/keywords/thinning","display_name":"Thinning","score":0.44541269540786743},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43616771697998047},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.414899080991745},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3238769471645355},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.14393308758735657},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09841957688331604}],"concepts":[{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.8521655797958374},{"id":"https://openalex.org/C16757284","wikidata":"https://www.wikidata.org/wiki/Q1145117","display_name":"Poisson point process","level":3,"score":0.6279759407043457},{"id":"https://openalex.org/C72010251","wikidata":"https://www.wikidata.org/wiki/Q5265688","display_name":"Determinantal point process","level":4,"score":0.6225971579551697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5068355202674866},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.4908321499824524},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.47919997572898865},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4574418067932129},{"id":"https://openalex.org/C2781353100","wikidata":"https://www.wikidata.org/wiki/Q1266974","display_name":"Thinning","level":2,"score":0.44541269540786743},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43616771697998047},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.414899080991745},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3238769471645355},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.14393308758735657},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09841957688331604},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wcnc.2019.8885526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcnc.2019.8885526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Wireless Communications and Networking Conference (WCNC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.08672","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.08672","pdf_url":"https://arxiv.org/pdf/1810.08672","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.08672","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.08672","pdf_url":"https://arxiv.org/pdf/1810.08672","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982313009.pdf","grobid_xml":"https://content.openalex.org/works/W2982313009.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1582938912","https://openalex.org/W1584023832","https://openalex.org/W1659111644","https://openalex.org/W1958800402","https://openalex.org/W1968543151","https://openalex.org/W1991814714","https://openalex.org/W1995313851","https://openalex.org/W2006672084","https://openalex.org/W2052021539","https://openalex.org/W2069356492","https://openalex.org/W2076773434","https://openalex.org/W2083809226","https://openalex.org/W2088288129","https://openalex.org/W2091038426","https://openalex.org/W2091725584","https://openalex.org/W2092812368","https://openalex.org/W2118166339","https://openalex.org/W2138779671","https://openalex.org/W2154090186","https://openalex.org/W2177179288","https://openalex.org/W2185588987","https://openalex.org/W2296650473","https://openalex.org/W2584234609","https://openalex.org/W2799837476","https://openalex.org/W2950970581","https://openalex.org/W2963303566","https://openalex.org/W3103014337","https://openalex.org/W3104324249","https://openalex.org/W4210862026","https://openalex.org/W4250310729","https://openalex.org/W4296588834","https://openalex.org/W4297271037","https://openalex.org/W6682418119"],"related_works":["https://openalex.org/W2951327448","https://openalex.org/W3112662864","https://openalex.org/W4289418610","https://openalex.org/W3104681144","https://openalex.org/W2963121780","https://openalex.org/W1994267277","https://openalex.org/W2896077940","https://openalex.org/W2982313009","https://openalex.org/W2963936628","https://openalex.org/W2412514802"],"abstract_inverted_index":{"A":[0],"new":[1,41,59],"type":[2],"of":[3,18,57,111],"dependent":[4],"thinning":[5,161,173],"for":[6,93,133],"point":[7,20,42,60,87,107,127],"processes":[8,21,128],"in":[9,100],"continuous":[10],"space":[11],"is":[12,29,99],"proposed,":[13],"which":[14,89],"leverages":[15],"the":[16,58,66,69,84,115,139,162,183],"advantages":[17],"determinantal":[19,126,160],"defined":[22],"on":[23,125,175],"finite":[24,105],"spaces":[25],"and,":[26],"as":[27,47,63,72,74,168,170],"such,":[28],"particularly":[30],"amenable":[31],"to":[32,95,141,144,188],"statistical,":[33],"numerical,":[34],"and":[35,55],"simulation":[36],"techniques.":[37],"It":[38],"gives":[39],"a":[40,48,171],"process":[43],"that":[44],"can":[45,78,90,186],"serve":[46],"network":[49,146],"model":[50],"exhibiting":[51],"repulsion.":[52],"The":[53],"properties":[54],"functions":[56],"process,":[61,88],"such":[62],"moment":[64],"measures,":[65],"Laplace":[67],"functional,":[68],"void":[70],"probabilities,":[71],"well":[73,131,169],"conditional":[75],"(Palm)":[76],"characteristics":[77],"be":[79,91,96,142],"estimated":[80],"accurately":[81],"by":[82,157],"simulating":[83],"underlying":[85],"(non-thinned)":[86],"taken,":[92],"example,":[94],"Poisson.":[97],"This":[98],"contrast":[101],"(and":[102],"preference":[103],"to)":[104],"Gibbs":[106],"processes,":[108],"which,":[109],"instead":[110],"thinning,":[112,167],"require":[113],"weighting":[114],"Poisson":[116],"realizations,":[117],"involving":[118],"usually":[119],"intractable":[120],"normalizing":[121],"constants.":[122],"Models":[123],"based":[124],"are":[129],"also":[130],"suited":[132],"statistical":[134],"(supervised)":[135],"learning":[136],"techniques,":[137],"allowing":[138],"models":[140],"fitted":[143],"observed":[145],"patterns":[147],"with":[148,159],"some":[149],"particular":[150],"geometric":[151],"properties.":[152],"We":[153],"illustrate":[154],"this":[155],"approach":[156,185],"imitating":[158],"well-known":[163],"Mat\u00e9rn":[164],"II":[165],"hard-core":[166],"soft-core":[172],"depending":[174],"nearest-neighbour":[176],"triangles.":[177],"These":[178],"two":[179],"examples":[180],"demonstrate":[181],"how":[182],"proposed":[184],"lead":[187],"new,":[189],"statistically":[190],"optimized,":[191],"probabilistic":[192],"transmission":[193],"scheduling":[194],"schemes.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2019-11-08T00:00:00"}
