{"id":"https://openalex.org/W7164042355","doi":"https://doi.org/10.1145/3798129.3800903","title":"Learning Mixture Models via Efficient High-Dimensional Sparse Fourier Transforms","display_name":"Learning Mixture Models via Efficient High-Dimensional Sparse Fourier Transforms","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164042355","doi":"https://doi.org/10.1145/3798129.3800903"},"language":null,"primary_location":{"id":"doi:10.1145/3798129.3800903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800903","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":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3798129.3800903","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042852153","display_name":"Alkis Kalavasis","orcid":"https://orcid.org/0000-0002-1479-5268"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alkis Kalavasis","raw_affiliation_strings":["Yale University, New Haven, USA"],"raw_orcid":"https://orcid.org/0000-0002-1479-5268","affiliations":[{"raw_affiliation_string":"Yale University, New Haven, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068103506","display_name":"Pravesh K. Kothari","orcid":"https://orcid.org/0000-0002-8689-6770"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pravesh K. Kothari","raw_affiliation_strings":["Princeton University, Princeton, USA"],"raw_orcid":"https://orcid.org/0000-0002-8689-6770","affiliations":[{"raw_affiliation_string":"Princeton University, Princeton, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122548937","display_name":"Shuchen Li","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuchen Li","raw_affiliation_strings":["Yale University, New Haven, USA"],"raw_orcid":"https://orcid.org/0009-0007-6653-7889","affiliations":[{"raw_affiliation_string":"Yale University, New Haven, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083461583","display_name":"Manolis Zampetakis","orcid":"https://orcid.org/0009-0005-4967-5927"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Manolis Zampetakis","raw_affiliation_strings":["Yale University, New Haven, USA"],"raw_orcid":"https://orcid.org/0009-0005-4967-5927","affiliations":[{"raw_affiliation_string":"Yale University, New Haven, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042852153"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96709202,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1984","last_page":"1994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2720000147819519,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2720000147819519,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.1671999990940094,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.16500000655651093,"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/mixture-model","display_name":"Mixture model","score":0.6007999777793884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3711000084877014},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.36559998989105225},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.3125999867916107},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.30630001425743103},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2996000051498413}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6007999777793884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5041999816894531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46939998865127563},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45579999685287476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3758000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30630001425743103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.290800005197525},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.2669999897480011},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3798129.3800903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800903","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"}],"best_oa_location":{"id":"doi:10.1145/3798129.3800903","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800903","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":39,"referenced_works":["https://openalex.org/W1574816920","https://openalex.org/W1796325169","https://openalex.org/W1824047490","https://openalex.org/W1956647075","https://openalex.org/W1974088667","https://openalex.org/W1981367467","https://openalex.org/W2012828271","https://openalex.org/W2014565165","https://openalex.org/W2022852240","https://openalex.org/W2026302946","https://openalex.org/W2047064469","https://openalex.org/W2057214765","https://openalex.org/W2060929722","https://openalex.org/W2062817469","https://openalex.org/W2205172111","https://openalex.org/W2214878575","https://openalex.org/W2313543494","https://openalex.org/W2771009994","https://openalex.org/W2773686537","https://openalex.org/W2808754399","https://openalex.org/W2942689850","https://openalex.org/W2962765673","https://openalex.org/W2963351358","https://openalex.org/W2963496884","https://openalex.org/W2964164735","https://openalex.org/W2964288084","https://openalex.org/W3002207671","https://openalex.org/W3034730172","https://openalex.org/W3116934360","https://openalex.org/W3129046222","https://openalex.org/W3170087187","https://openalex.org/W3170755383","https://openalex.org/W4313227216","https://openalex.org/W4316652387","https://openalex.org/W4386089604","https://openalex.org/W4406354910","https://openalex.org/W4411319850","https://openalex.org/W4411319966","https://openalex.org/W7128638666"],"related_works":[],"abstract_inverted_index":null,"counts_by_year":[],"updated_date":"2026-06-10T14:10:52.464848","created_date":"2026-06-10T00:00:00"}