{"id":"https://openalex.org/W2767023880","doi":"https://doi.org/10.1145/3128572.3140447","title":"Robust Linear Regression Against Training Data Poisoning","display_name":"Robust Linear Regression Against Training Data Poisoning","publication_year":2017,"publication_date":"2017-11-03","ids":{"openalex":"https://openalex.org/W2767023880","doi":"https://doi.org/10.1145/3128572.3140447","mag":"2767023880"},"language":"en","primary_location":{"id":"doi:10.1145/3128572.3140447","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3128572.3140447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security","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/A5100353208","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0002-1537-561X"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chang Liu","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374429","display_name":"Bo Li","orcid":"https://orcid.org/0000-0002-4456-7303"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038669899","display_name":"Yevgeniy Vorobeychik","orcid":"https://orcid.org/0000-0003-2471-5345"},"institutions":[{"id":"https://openalex.org/I200719446","display_name":"Vanderbilt University","ror":"https://ror.org/02vm5rt34","country_code":"US","type":"education","lineage":["https://openalex.org/I200719446"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yevgeniy Vorobeychik","raw_affiliation_strings":["Vanderbilt University, Nashville, TN, USA"],"affiliations":[{"raw_affiliation_string":"Vanderbilt University, Nashville, TN, USA","institution_ids":["https://openalex.org/I200719446"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035574749","display_name":"Alina Oprea","orcid":"https://orcid.org/0000-0002-4979-5292"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alina Oprea","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100353208"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":9.3639,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.99003209,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"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":0.9987999796867371,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9973000288009644,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9825999736785889,"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/computer-science","display_name":"Computer science","score":0.6320761442184448},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6304303407669067},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6213383078575134},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.567255973815918},{"id":"https://openalex.org/keywords/robust-regression","display_name":"Robust regression","score":0.5330081582069397},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5142040252685547},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5009365081787109},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4965541958808899},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.44704627990722656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43333667516708374},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4139532148838043},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2379138171672821},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22358855605125427},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1770375669002533}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6320761442184448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6304303407669067},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6213383078575134},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.567255973815918},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.5330081582069397},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5142040252685547},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5009365081787109},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4965541958808899},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.44704627990722656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43333667516708374},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4139532148838043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2379138171672821},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22358855605125427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1770375669002533},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3128572.3140447","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3128572.3140447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2901424354","display_name":null,"funder_award_id":"IIS-1526860","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G371206825","display_name":null,"funder_award_id":"CNS-1238","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4908473902","display_name":null,"funder_award_id":"CNS-1640624","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5385707076","display_name":null,"funder_award_id":"IIS-1649972","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6415979052","display_name":null,"funder_award_id":"TWC-1409915","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W145450961","https://openalex.org/W290150691","https://openalex.org/W1551848773","https://openalex.org/W1613169971","https://openalex.org/W1664169458","https://openalex.org/W1853481526","https://openalex.org/W1987423623","https://openalex.org/W1999993677","https://openalex.org/W2004026774","https://openalex.org/W2007562169","https://openalex.org/W2012929908","https://openalex.org/W2071128523","https://openalex.org/W2107555749","https://openalex.org/W2109394932","https://openalex.org/W2112507308","https://openalex.org/W2128155976","https://openalex.org/W2145962650","https://openalex.org/W2151298633","https://openalex.org/W2159294971","https://openalex.org/W2167421362","https://openalex.org/W2293768274","https://openalex.org/W2293844262","https://openalex.org/W2296452361","https://openalex.org/W2405680064","https://openalex.org/W2554864439","https://openalex.org/W2557044351","https://openalex.org/W2597603852","https://openalex.org/W2740586183","https://openalex.org/W2950673872","https://openalex.org/W3099514962","https://openalex.org/W4247950230","https://openalex.org/W4299439299"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W4391160746","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241"],"abstract_inverted_index":{"The":[0],"effectiveness":[1],"of":[2,40,51,74,142],"supervised":[3,17,65],"learning":[4,18,41,66],"techniques":[5,115],"has":[6,42],"made":[7,43],"them":[8],"ubiquitous":[9],"in":[10,33,146],"research":[11],"and":[12,27,82,122,127,149],"practice.":[13],"In":[14],"high-dimensional":[15],"settings,":[16],"commonly":[19],"relies":[20],"on":[21,68],"dimensionality":[22],"reduction":[23],"to":[24,61],"improve":[25],"performance":[26,130],"identify":[28],"the":[29,37,72,75,103,143],"most":[30],"important":[31],"factors":[32],"predicting":[34],"outcomes.":[35],"However,":[36],"economic":[38],"importance":[39],"it":[44],"a":[45,111],"natural":[46],"target":[47],"for":[48,93],"adversarial":[49],"manipulation":[50],"training":[52],"data,":[53],"which":[54],"we":[55,133],"term":[56],"poisoning":[57],"attacks.":[58],"Prior":[59],"approaches":[60],"dealing":[62],"with":[63,85],"robust":[64,94,118,123],"rely":[67],"strong":[69,129],"assumptions":[70],"about":[71],"nature":[73],"feature":[76,80,104],"matrix,":[77],"such":[78],"as":[79],"independence":[81],"sub-Gaussian":[83],"noise":[84],"low":[86],"variance.":[87],"We":[88],"propose":[89],"an":[90],"integrated":[91],"method":[92],"regression":[95],"that":[96,102,136],"relaxes":[97],"these":[98],"assumptions,":[99],"assuming":[100],"only":[101],"matrix":[105,120],"can":[106],"be":[107],"well":[108],"approximated":[109],"by":[110],"low-rank":[112,119],"matrix.":[113],"Our":[114],"integrate":[116],"improved":[117],"approximation":[121],"principle":[124],"component":[125],"regression,":[126],"yield":[128],"guarantees.":[131],"Moreover,":[132],"experimentally":[134],"show":[135],"our":[137],"methods":[138],"significantly":[139],"outperform":[140],"state":[141],"art":[144],"both":[145],"running":[147],"time":[148],"prediction":[150],"error.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}