{"id":"https://openalex.org/W7140425499","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443850","title":"Non-negative DAG Learning from Time-Series Data","display_name":"Non-negative DAG Learning from Time-Series Data","publication_year":2025,"publication_date":"2025-10-26","ids":{"openalex":"https://openalex.org/W7140425499","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443850"},"language":null,"primary_location":{"id":"doi:10.1109/ieeeconf67917.2025.11443850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"},"type":"article","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/A5114367813","display_name":"Samuel Rey","orcid":null},"institutions":[{"id":"https://openalex.org/I182083151","display_name":"Universidad Rey Juan Carlos","ror":"https://ror.org/01v5cv687","country_code":"ES","type":"education","lineage":["https://openalex.org/I182083151"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Samuel Rey","raw_affiliation_strings":["Rey Juan Carlos University,Dept. of Signal Theory and Communications,Madrid,Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rey Juan Carlos University,Dept. of Signal Theory and Communications,Madrid,Spain","institution_ids":["https://openalex.org/I182083151"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006078163","display_name":"Gonzalo Mateos","orcid":"https://orcid.org/0000-0002-9847-6298"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo Mateos","raw_affiliation_strings":["University of Rochester,Dept. of Electrical and Computer Engineering,NY,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Rochester,Dept. of Electrical and Computer Engineering,NY,USA","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68089729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"930","last_page":"934"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.21459999680519104,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.21459999680519104,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10320","display_name":"Neural Networks and Applications","score":0.0729999989271164,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.03889999911189079,"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/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.8396000266075134},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6930999755859375},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4912000000476837},{"id":"https://openalex.org/keywords/directed-graph","display_name":"Directed graph","score":0.4839000105857849},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4616999924182892},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4350999891757965},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.41819998621940613},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.38940000534057617},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.387800008058548}],"concepts":[{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.8396000266075134},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6930999755859375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.546500027179718},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.527999997138977},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4912000000476837},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4025000035762787},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.35690000653266907},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34150001406669617},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C206194317","wikidata":"https://www.wikidata.org/wiki/Q1138624","display_name":"Convex hull","level":3,"score":0.2969000041484833},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C53052385","wikidata":"https://www.wikidata.org/wiki/Q17104046","display_name":"Mixed graph","level":5,"score":0.29330000281333923},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C177384507","wikidata":"https://www.wikidata.org/wiki/Q1149000","display_name":"Multivariate normal distribution","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf67917.2025.11443850","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443850","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1567331820","https://openalex.org/W2020294948","https://openalex.org/W2029800614","https://openalex.org/W2033105784","https://openalex.org/W2132555912","https://openalex.org/W2157069980","https://openalex.org/W2615556757","https://openalex.org/W2798585159","https://openalex.org/W2962759781","https://openalex.org/W2964012239","https://openalex.org/W2994097903","https://openalex.org/W3084183742","https://openalex.org/W3095449605","https://openalex.org/W3162590762","https://openalex.org/W3208382082","https://openalex.org/W3212653422","https://openalex.org/W4250375351","https://openalex.org/W4250589301","https://openalex.org/W4302423442","https://openalex.org/W4385732031","https://openalex.org/W4388271908","https://openalex.org/W4392911148","https://openalex.org/W4395056663","https://openalex.org/W4408355322","https://openalex.org/W4409156873","https://openalex.org/W4415367329"],"related_works":[],"abstract_inverted_index":{"This":[0,104],"work":[1],"aims":[2],"to":[3,46,70,77,97,107],"learn":[4],"the":[5,12,38,65,84,109,131,136,143,148,151],"directed":[6],"acyclic":[7],"graph":[8],"(DAG)":[9],"that":[10,74,83],"captures":[11],"instantaneous":[13,33,39],"dependencies":[14],"underlying":[15,85],"a":[16,24,44,101,120],"multivariate":[17,116],"time":[18],"series.":[19],"The":[20],"observed":[21],"data":[22,118],"follow":[23],"linear":[25],"structural":[26],"vector":[27],"autoregressive":[28],"model":[29],"(SVARM)":[30],"with":[31],"both":[32],"and":[34,92],"time-lagged":[35],"dependencies,":[36],"where":[37,158],"structure":[40,96],"is":[41],"modeled":[42],"by":[43],"DAG":[45,86,113],"reflect":[47],"potential":[48],"causal":[49],"relationships.":[50],"While":[51],"recent":[52],"continuous":[53],"relaxation":[54],"approaches":[55],"impose":[56,98],"acyclicity":[57,99],"through":[58],"smooth":[59],"constraint":[60],"functions":[61],"involving":[62],"powers":[63],"of":[64,111,133,142,150],"adjacency":[66],"matrix,":[67],"they":[68],"lead":[69],"non-convex":[71],"optimization":[72,122],"problems":[73],"are":[75],"challenging":[76],"solve.":[78],"In":[79],"contrast,":[80],"we":[81,128,146],"assume":[82],"has":[87],"only":[88],"non-negative":[89,112],"edge":[90],"weights,":[91],"leverage":[93],"this":[94],"additional":[95],"via":[100],"convex":[102,121,137],"constraint.":[103],"enables":[105],"us":[106],"cast":[108],"problem":[110,123],"recovery":[114],"from":[115],"time-series":[117,156],"as":[119],"in":[124],"abstract":[125],"form,":[126],"which":[127],"solve":[129],"using":[130],"method":[132,153],"multipliers.":[134],"Crucially,":[135],"formulation":[138],"guarantees":[139],"global":[140],"optimality":[141],"solution.":[144],"Finally,":[145],"assess":[147],"performance":[149],"proposed":[152],"on":[154],"synthetic":[155],"data,":[157],"it":[159],"outperforms":[160],"existing":[161],"alternatives.":[162]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-27T00:00:00"}