{"id":"https://openalex.org/W4416386201","doi":"https://doi.org/10.1145/3798129.3800905","title":"Computational and Statistical Lower Bounds for Low-Rank Estimation under General Inhomogeneous Noise","display_name":"Computational and Statistical Lower Bounds for Low-Rank Estimation under General Inhomogeneous Noise","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W4416386201","doi":"https://doi.org/10.1145/3798129.3800905"},"language":"en","primary_location":{"id":"doi:10.1145/3798129.3800905","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800905","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3798129.3800905","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080194478","display_name":"Debsurya De","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2799853436","display_name":"Johns Hopkins Medicine","ror":"https://ror.org/037zgn354","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799853436"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Debsurya De","raw_affiliation_strings":["Johns Hopkins University, Applied Mathematics &amp; Statistics, Baltimore, USA"],"raw_orcid":"https://orcid.org/0000-0002-5360-3871","affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Applied Mathematics &amp; Statistics, Baltimore, USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044093947","display_name":"Dmitriy Kunisky","orcid":"https://orcid.org/0000-0002-0854-4067"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]},{"id":"https://openalex.org/I2799853436","display_name":"Johns Hopkins Medicine","ror":"https://ror.org/037zgn354","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799853436"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitriy Kunisky","raw_affiliation_strings":["Johns Hopkins University, Applied Mathematics &amp; Statistics, Baltimore, USA"],"raw_orcid":"https://orcid.org/0000-0002-0854-4067","affiliations":[{"raw_affiliation_string":"Johns Hopkins University, Applied Mathematics &amp; Statistics, Baltimore, USA","institution_ids":["https://openalex.org/I145311948","https://openalex.org/I2799853436"]}]}],"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":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01442231,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2007","last_page":"2018"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8191999793052673,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8191999793052673,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.12030000239610672,"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/T12303","display_name":"Tensor decomposition and applications","score":0.010099999606609344,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/gaussian","display_name":"Gaussian","score":0.5055000185966492},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.4546000063419342},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4542999863624573},{"id":"https://openalex.org/keywords/random-matrix","display_name":"Random matrix","score":0.4458000063896179},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.43369999527931213},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4309000074863434},{"id":"https://openalex.org/keywords/conjecture","display_name":"Conjecture","score":0.42010000348091125},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.39890000224113464},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.39480000734329224}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7476999759674072},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5612999796867371},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.4546000063419342},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4542999863624573},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4472000002861023},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.4458000063896179},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C2780990831","wikidata":"https://www.wikidata.org/wiki/Q319141","display_name":"Conjecture","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.358599990606308},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.3479999899864197},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.32850000262260437},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C108710211","wikidata":"https://www.wikidata.org/wiki/Q11538","display_name":"Mathematical proof","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C23463724","wikidata":"https://www.wikidata.org/wiki/Q2308831","display_name":"Spectral method","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2554999887943268}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3798129.3800905","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800905","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.08541","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.08541","pdf_url":"https://arxiv.org/pdf/2510.08541","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.08541","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.08541","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.1145/3798129.3800905","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800905","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"work":[1],"has":[2,31,45,187],"generalized":[3],"several":[4],"results":[5,52,93,174,179],"concerning":[6],"the":[7,25,29,38,42,62,65,87,96,116,133,172,184,194,227],"well-understood":[8],"spiked":[9],"Wigner":[10],"matrix":[11,17],"model":[12],"of":[13,51,99,112,141,161,171,210,216,230,261],"a":[14,32,46,49,108,121,137,159,188,207,217,231],"low-rank":[15,122],"signal":[16,67,84,113,162],"corrupted":[18],"by":[19,94,166],"additive":[20],"i.i.d.":[21],"Gaussian":[22],"noise":[23,30],"to":[24,73],"inhomogeneous":[26,176],"case,":[27],"where":[28,41],"variance":[33,43,185],"profile.":[34,222],"In":[35],"particular,":[36],"for":[37,58,70,81,107,136,158,201,213,255],"special":[39,83],"case":[40],"profile":[44,186],"block":[47,189],"structure,":[48,190],"series":[50],"identified":[53,64],"an":[54,214],"effective":[55],"spectral":[56,101,117,196],"algorithm":[57,72,118,197],"detecting":[59],"and":[60,75,145,191,238],"estimating":[61],"signal,":[63,123],"threshold":[66],"strength":[68],"required":[69],"that":[71,183,193,249],"succeed,":[74],"proved":[76],"information-theoretic":[77,155],"lower":[78,156],"bounds":[79,157,245],"that,":[80,106],"some":[82],"distributions,":[85,114],"match":[86],"above":[88,173],"threshold.":[89],"We":[90,205],"complement":[91],"these":[92,247],"studying":[95],"computational":[97,138],"optimality":[98],"this":[100,211],"algorithm.":[102,130],"Namely,":[103],"we":[104,151,242],"show":[105],"much":[109],"broader":[110],"range":[111],"whenever":[115],"cannot":[119],"detect":[120],"then":[124],"neither":[125],"can":[126],"any":[127],"low-degree":[128],"polynomial":[129],"This":[131],"gives":[132],"first":[134],"evidence":[135],"hardness":[139],"conjecture":[140],"Guionnet,":[142],"Ko,":[143],"Krzakala,":[144],"Zdeborov\u00e1":[146],"(2023).":[147],"With":[148],"similar":[149],"techniques,":[150],"also":[152,234],"prove":[153],"sharp":[154],"class":[160],"distributions":[163],"not":[164,181],"treated":[165],"prior":[167],"work.":[168],"Unlike":[169],"all":[170],"on":[175,246],"models,":[177],"our":[178],"do":[180],"assume":[182],"suggest":[192],"same":[195],"might":[198],"remain":[199],"optimal":[200],"quite":[202],"general":[203],"profiles.":[204],"include":[206],"numerical":[208],"study":[209],"claim":[212],"example":[215],"smoothly-varying":[218],"rather":[219],"than":[220,252],"piecewise-constant":[221],"Our":[223],"proofs":[224],"involve":[225],"analyzing":[226],"graph":[228],"sums":[229],"matrix,":[232],"which":[233,258],"appear":[235],"in":[236],"free":[237],"traffic":[239],"probability,":[240],"but":[241],"require":[243],"new":[244],"quantities":[248],"are":[250],"tighter":[251],"existing":[253],"ones":[254],"non-negative":[256],"matrices,":[257],"may":[259],"be":[260],"independent":[262],"interest.":[263]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-11T00:00:00"}
