{"id":"https://openalex.org/W7163332507","doi":"https://doi.org/10.1109/tsp.2026.3699618","title":"Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning Using Neural Networks","display_name":"Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning Using Neural Networks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7163332507","doi":"https://doi.org/10.1109/tsp.2026.3699618"},"language":null,"primary_location":{"id":"doi:10.1109/tsp.2026.3699618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2026.3699618","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-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/A5089242792","display_name":"Rushabha Balaji","orcid":"https://orcid.org/0009-0002-7115-5432"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rushabha Balaji","raw_affiliation_strings":["University of California, Los Angeles"],"raw_orcid":"https://orcid.org/0009-0002-7115-5432","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133055643","display_name":"Kuan-Lin Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kuan-Lin Chen","raw_affiliation_strings":["University of California, San Diego"],"raw_orcid":"https://orcid.org/0009-0005-4067-0927","affiliations":[{"raw_affiliation_string":"University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134146462","display_name":"Danijela Cabric","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danijela Cabric","raw_affiliation_strings":["University of California, Los Angeles"],"raw_orcid":"https://orcid.org/0000-0002-5967-2683","affiliations":[{"raw_affiliation_string":"University of California, Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5137800813","display_name":"Bhaskar D. Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhaskar D. Rao","raw_affiliation_strings":["University of California, San Diego"],"raw_orcid":"https://orcid.org/0000-0001-6357-689X","affiliations":[{"raw_affiliation_string":"University of California, San Diego","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"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":0.68390733,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":null,"first_page":"2476","last_page":"2490"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.15189999341964722,"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":0.15189999341964722,"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":0.12269999831914902,"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":0.07750000059604645,"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/wake-sleep-algorithm","display_name":"Wake-sleep algorithm","score":0.5788999795913696},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5509999990463257},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.439300000667572},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4162999987602234},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40869998931884766},{"id":"https://openalex.org/keywords/instance-based-learning","display_name":"Instance-based learning","score":0.40310001373291016},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.3961000144481659},{"id":"https://openalex.org/keywords/probably-approximately-correct-learning","display_name":"Probably approximately correct learning","score":0.3808000087738037},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3682999908924103},{"id":"https://openalex.org/keywords/types-of-artificial-neural-networks","display_name":"Types of artificial neural networks","score":0.3614000082015991}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7612000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499999761581421},{"id":"https://openalex.org/C17061570","wikidata":"https://www.wikidata.org/wiki/Q7960888","display_name":"Wake-sleep algorithm","level":4,"score":0.5788999795913696},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5509999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5045999884605408},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43369999527931213},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.40310001373291016},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.3808000087738037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.3614000082015991},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.304500013589859},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2955999970436096},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C50292564","wikidata":"https://www.wikidata.org/wiki/Q2462783","display_name":"Computational learning theory","level":3,"score":0.2912999987602234},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.2856000065803528},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2026.3699618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2026.3699618","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5506274774","display_name":null,"funder_award_id":"CCF-2225617","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W45374770","https://openalex.org/W143004564","https://openalex.org/W653761051","https://openalex.org/W866015105","https://openalex.org/W1667249920","https://openalex.org/W1733556918","https://openalex.org/W2041101642","https://openalex.org/W2065743591","https://openalex.org/W2100556411","https://openalex.org/W2103496339","https://openalex.org/W2107861471","https://openalex.org/W2122315118","https://openalex.org/W2127870457","https://openalex.org/W2128659236","https://openalex.org/W2148154358","https://openalex.org/W2161765392","https://openalex.org/W2162409952","https://openalex.org/W2168745297","https://openalex.org/W2194775991","https://openalex.org/W2290170958","https://openalex.org/W2342863993","https://openalex.org/W2489539531","https://openalex.org/W2508393166","https://openalex.org/W2783180185","https://openalex.org/W2911546748","https://openalex.org/W2920309202","https://openalex.org/W2937977811","https://openalex.org/W3133902371","https://openalex.org/W3174707381","https://openalex.org/W4323914160","https://openalex.org/W4327808052","https://openalex.org/W4388118100","https://openalex.org/W4404035199","https://openalex.org/W4408353603","https://openalex.org/W4413472385","https://openalex.org/W7133241541"],"related_works":[],"abstract_inverted_index":{"Sparse":[0],"Bayesian":[1],"Learning":[2],"(SBL)":[3],"is":[4,33,45,220],"one":[5],"of":[6,52,91,144,151,176,184,232,256,264,278,309],"the":[7,19,38,71,80,100,111,142,149,153,160,167,172,182,208,230,233,246,254,259,262,265,273,279],"most":[8,72,102],"popular":[9,73,103],"sparse":[10,29,215],"signal":[11],"recovery":[12,30,216],"methods,":[13],"and":[14,27,135,147,187,236,271,313],"various":[15],"algorithms":[16,75],"exist":[17],"under":[18,110],"SBL":[20,58,74,92,104,145,155,185,193],"paradigm.":[21],"However,":[22],"given":[23],"a":[24,28,50,53,121,125,191,200,241,289],"performance":[25,295,305],"metric":[26],"problem,":[31],"it":[32],"difficult":[34],"to":[35,41,49,56,88,179,189,244,261,269,287],"know":[36],"a-priori":[37],"best":[39,154],"algorithm":[40,156],"choose.":[42],"This":[43],"difficulty":[44],"in":[46],"part":[47],"due":[48],"lack":[51],"unified":[54],"framework":[55,113,169],"derive":[57],"algorithms.":[59,131],"In":[60,253],"this":[61,65,89,133],"work,":[62],"we":[63,97,140,164,301],"address":[64,148],"issue":[66],"by":[67,170,292],"first":[68],"showing":[69],"that":[70,99,205,223],"can":[76,206],"be":[77],"derived":[78],"using":[79],"majorization-minimization":[81],"(MM)":[82],"principle.":[83],"Hence,":[84],"providing":[85],"convergence":[86],"guarantees":[87],"class":[90,143,183],"methods":[93],"hitherto":[94],"unknown.":[95],"Moreover,":[96],"show":[98],"two":[101],"update":[105,194],"rules":[106],"not":[107,227],"only":[108],"fall":[109],"MM":[112,138,161,168,210],"but":[114],"are":[115],"both":[116],"valid":[117],"descent":[118],"steps":[119],"for":[120],"common":[122],"majorizer,":[123],"revealing":[124],"deeper":[126],"analytical":[127],"compatibility":[128],"between":[129],"these":[130],"Using":[132],"insight":[134],"properties":[136],"from":[137,196],"theory":[139],"expand":[141,181],"algorithms,":[146,186],"question":[150],"finding":[152],"via":[157],"data":[158],"within":[159],"framework.":[162],"Second,":[163],"go":[165],"beyond":[166],"introducing":[171],"powerful":[173],"modeling":[174],"capabilities":[175],"deep":[177,202],"learning":[178,203],"further":[180],"aim":[188],"learn":[190,288],"superior":[192],"rule":[195],"data.":[197],"We":[198,281],"propose":[199],"novel":[201],"architecture":[204,219],"outperform":[207],"classical":[209],"based":[211],"ones":[212],"across":[213,249,275,306],"different":[214,250,276,307],"problems.":[217],"Our":[218],"designed":[221],"such":[222],"its":[224,293],"complexity":[225],"does":[226],"scale":[228],"with":[229,240],"dimension":[231],"measurement":[234,251,298],"matrix,":[235],"hence":[237],"provides":[238],"us":[239,268],"unique":[242],"opportunity":[243],"test":[245,272,302],"generalization":[247],"capability":[248],"matrices.":[252,299],"case":[255],"parameterized":[257],"dictionaries,":[258],"invariance":[260],"size":[263],"matrix":[266],"allows":[267],"train":[270],"model":[274,304],"ranges":[277],"parameter.":[280],"also":[282],"showcase":[283],"our":[284,303],"models":[285],"ability":[286],"functional":[290],"mapping":[291],"zero-shot":[294],"on":[296],"unseen":[297],"Finally,":[300],"number":[308],"snapshots,":[310],"signal-to-noise":[311],"ratios":[312],"sparsity":[314],"levels.":[315]},"counts_by_year":[],"updated_date":"2026-07-23T05:56:39.545243","created_date":"2026-06-04T00:00:00"}
