{"id":"https://openalex.org/W3089778317","doi":"https://doi.org/10.48550/arxiv.2007.03641","title":"One-Bit Compressed Sensing via One-Shot Hard Thresholding","display_name":"One-Bit Compressed Sensing via One-Shot Hard Thresholding","publication_year":2020,"publication_date":"2020-07-07","ids":{"openalex":"https://openalex.org/W3089778317","doi":"https://doi.org/10.48550/arxiv.2007.03641","mag":"3089778317"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2007.03641","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.03641","pdf_url":"https://arxiv.org/pdf/2007.03641","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2007.03641","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101700975","display_name":"Jie Shen","orcid":"https://orcid.org/0000-0002-4440-7756","observed_orcids":["https://orcid.org/0000-0002-4440-7756"]},"institutions":[{"id":"https://openalex.org/I108468826","display_name":"Stevens Institute of Technology","ror":"https://ror.org/02z43xh36","country_code":"US","type":"education","lineage":["https://openalex.org/I108468826"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shen, Jie","raw_affiliation_strings":["Stevens Institute of Technology, Hoboken, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stevens Institute of Technology, Hoboken, United States","institution_ids":["https://openalex.org/I108468826"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101700975"],"corresponding_institution_ids":["https://openalex.org/I108468826"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"510","last_page":"519"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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/T10136","display_name":"Statistical Methods and Inference","score":0.0,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/one-bit-compressed-sensing","display_name":"one-bit compressed sensing","score":0.9169999957084656},{"id":"https://openalex.org/keywords/sparse-signal-recovery","display_name":"sparse signal recovery","score":0.7950000166893005},{"id":"https://openalex.org/keywords/binary-measurements","display_name":"binary measurements","score":0.8259999752044678},{"id":"https://openalex.org/keywords/gaussian-width","display_name":"Gaussian width","score":0.7269999980926514},{"id":"https://openalex.org/keywords/norm-estimation","display_name":"norm estimation","score":0.8930000066757202},{"id":"https://openalex.org/keywords/support-recovery","display_name":"support recovery","score":0.9990000128746033},{"id":"https://openalex.org/keywords/model-misspecification","display_name":"model misspecification","score":0.996999979019165},{"id":"https://openalex.org/keywords/l2-error-bounds","display_name":"L2 error bounds","score":0.4779999852180481},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.4480000138282776}],"concepts":[{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.7206096649169922},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.637662947177887},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.629228949546814},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6289457082748413},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6119365096092224},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6067456007003784},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5273550152778625},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5170629620552063},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4859086573123932},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4832402169704437},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.44575831294059753},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.42527303099632263},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3993384838104248},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31501305103302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19381332397460938},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15873044729232788},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10292854905128479},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.09677600860595703},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2007.03641","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.03641","pdf_url":"https://arxiv.org/pdf/2007.03641","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.2007.03641","doi":"https://doi.org/10.48550/arxiv.2007.03641","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2007.03641","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:3089778317","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v124/shen20b/shen20b.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306421103","display_name":"Uncertainty in Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Uncertainty in Artificial Intelligence","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2007.03641","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2007.03641","pdf_url":"https://arxiv.org/pdf/2007.03641","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W625557853","https://openalex.org/W1605194072","https://openalex.org/W1978494516","https://openalex.org/W1986931325","https://openalex.org/W2060430274","https://openalex.org/W2068693086","https://openalex.org/W2074682976","https://openalex.org/W2112038498","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2131172946","https://openalex.org/W2134474909","https://openalex.org/W2135046866","https://openalex.org/W2140156116","https://openalex.org/W2148924034","https://openalex.org/W2159700154","https://openalex.org/W2294622949","https://openalex.org/W2296616510","https://openalex.org/W2398269536","https://openalex.org/W2549916392","https://openalex.org/W2585256380","https://openalex.org/W2611851484","https://openalex.org/W2623734663","https://openalex.org/W2741226915","https://openalex.org/W2885568127","https://openalex.org/W2887346854","https://openalex.org/W2963322354"],"related_works":["https://openalex.org/W3041651214","https://openalex.org/W2294622949","https://openalex.org/W2962975464","https://openalex.org/W2168374944","https://openalex.org/W2169472615","https://openalex.org/W2345726393","https://openalex.org/W2950968140","https://openalex.org/W2111895931","https://openalex.org/W3099838995","https://openalex.org/W2072209051","https://openalex.org/W1981028967","https://openalex.org/W3012290627","https://openalex.org/W2038974542","https://openalex.org/W1511679446","https://openalex.org/W2741796371","https://openalex.org/W2900204339","https://openalex.org/W3122422392","https://openalex.org/W2946691486","https://openalex.org/W3106168702","https://openalex.org/W2736310305"],"abstract_inverted_index":{"This":[0],"paper":[1],"concerns":[2],"the":[3,10,42,66,72,95,102,125],"problem":[4],"of":[5,21,46,69,76,82,118],"1-bit":[6],"compressed":[7],"sensing,":[8],"where":[9],"goal":[11],"is":[12,58,99,113,129],"to":[13,60,65],"estimate":[14],"a":[15,19,27,33,55,135],"sparse":[16],"signal":[17,68],"from":[18,41],"few":[20],"its":[22],"binary":[23],"measurements.":[24],"We":[25,49],"study":[26],"non-convex":[28,103],"sparsity-constrained":[29],"program":[30,104],"and":[31,35,91,121],"present":[32],"novel":[34],"concise":[36],"analysis":[37],"that":[38,51,85,101,131],"moves":[39],"away":[40],"widely":[43],"used":[44],"notion":[45],"Gaussian":[47],"width.":[48],"show":[50],"with":[52],"high":[53],"probability":[54],"simple":[56],"algorithm":[57],"guaranteed":[59],"produce":[61],"an":[62,80],"accurate":[63],"approximation":[64],"normalized":[67],"interest":[70],"under":[71,139],"$\\ell_2$-metric.":[73],"On":[74,94,124],"top":[75],"that,":[77],"we":[78],"establish":[79],"ensemble":[81],"new":[83],"results":[84,144],"address":[86],"norm":[87],"estimation,":[88],"support":[89],"recovery,":[90],"model":[92],"misspecification.":[93],"computational":[96],"side,":[97,127],"it":[98,128],"shown":[100,130],"can":[105],"be":[106],"solved":[107],"via":[108],"one-step":[109],"hard":[110],"thresholding":[111],"which":[112],"dramatically":[114],"efficient":[115],"in":[116],"terms":[117],"time":[119],"complexity":[120],"memory":[122],"footprint.":[123],"statistical":[126],"our":[132],"estimator":[133],"enjoys":[134],"near-optimal":[136],"error":[137],"rate":[138],"standard":[140],"conditions.":[141],"The":[142],"theoretical":[143],"are":[145],"substantiated":[146],"by":[147],"numerical":[148],"experiments.":[149]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-09-29T14:09:16.559479","created_date":"2025-10-10T00:00:00"}
