{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:16Z","timestamp":1750220536295,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"AFOSR","award":["FA9550-18-1-0152"],"award-info":[{"award-number":["FA9550-18-1-0152"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1652131, IIS-1816986, BIGDATA-1838177"],"award-info":[{"award-number":["IIS-1652131, IIS-1816986, BIGDATA-1838177"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,9]]},"DOI":"10.1145\/3448016.3457327","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T17:22:30Z","timestamp":1624036950000},"page":"352-364","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix"],"prefix":"10.1145","author":[{"given":"Zhenwei","family":"Dai","sequence":"first","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Aditya","family":"Desai","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]},{"given":"Reinhard","family":"Heckel","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]},{"given":"Anshumali","family":"Shrivastava","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"et almbox","author":"Abadi Mart'in","year":"2016","unstructured":"Mart'in Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , et almbox . 2016 . Tensorflow : A system for large-scale machine learning. In 12th $$USENIX$$ symposium on operating systems design and implementation ($$OSDI$$ 16) . 265--283. Mart'in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et almbox. 2016. Tensorflow: A system for large-scale machine learning. In 12th $$USENIX$$ symposium on operating systems design and implementation ($$OSDI$$ 16) . 265--283."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CAMSAP.2015.7383810"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asr054"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"volume-title":"International Colloquium on Automata, Languages, and Programming","author":"Charikar Moses","key":"e_1_3_2_2_5_1","unstructured":"Moses Charikar , Kevin Chen , and Martin Farach-Colton . 2002. Finding frequent items in data streams . In International Colloquium on Automata, Languages, and Programming . Springer , 693--703. Moses Charikar, Kevin Chen, and Martin Farach-Colton. 2002. Finding frequent items in data streams. In International Colloquium on Automata, Languages, and Programming. Springer, 693--703."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2014.6875187"},{"volume-title":"Covariance of the Wishart Distribution with Applications to Regression. Department of Mathematics and of Statistics","author":"Christensen Ronald","key":"e_1_3_2_2_7_1","unstructured":"Ronald Christensen . 2015. Covariance of the Wishart Distribution with Applications to Regression. Department of Mathematics and of Statistics , University of New Mexico (2015) . Ronald Christensen. 2015. Covariance of the Wishart Distribution with Applications to Regression. Department of Mathematics and of Statistics, University of New Mexico (2015)."},{"key":"e_1_3_2_2_8_1","volume-title":"Fast Sketch-based Recovery of Correlation Outliers. arXiv preprint arXiv:1710.01985","author":"Cormode Graham","year":"2017","unstructured":"Graham Cormode and Jacques Dark . 2017. Fast Sketch-based Recovery of Correlation Outliers. arXiv preprint arXiv:1710.01985 ( 2017 ). Graham Cormode and Jacques Dark. 2017. Fast Sketch-based Recovery of Correlation Outliers. arXiv preprint arXiv:1710.01985 (2017)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/ectj.12061"},{"key":"e_1_3_2_2_10_1","volume-title":"Canonical correlation analysis: An overview with application to learning methods. Neural computation","author":"Hardoon David R","year":"2004","unstructured":"David R Hardoon , Sandor Szedmak , and John Shawe-Taylor . 2004. Canonical correlation analysis: An overview with application to learning methods. Neural computation , Vol. 16 , 12 ( 2004 ), 2639--2664. David R Hardoon, Sandor Szedmak, and John Shawe-Taylor. 2004. Canonical correlation analysis: An overview with application to learning methods. Neural computation , Vol. 16, 12 (2004), 2639--2664."},{"key":"e_1_3_2_2_11_1","volume-title":"Conference on Learning Theory. 423--439","author":"Jamieson Kevin","year":"2014","unstructured":"Kevin Jamieson , Matthew Malloy , Robert Nowak , and S\u00e9bastien Bubeck . 2014 . lil'ucb: An optimal exploration algorithm for multi-armed bandits . In Conference on Learning Theory. 423--439 . Kevin Jamieson, Matthew Malloy, Robert Nowak, and S\u00e9bastien Bubeck. 2014. lil'ucb: An optimal exploration algorithm for multi-armed bandits. In Conference on Learning Theory. 423--439."},{"volume-title":"Deep learning with python","author":"Ketkar Nikhil","key":"e_1_3_2_2_12_1","unstructured":"Nikhil Ketkar . 2017. Introduction to pytorch . In Deep learning with python . Springer , 195--208. Nikhil Ketkar. 2017. Introduction to pytorch. In Deep learning with python . Springer, 195--208."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01454828"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1497494"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2493252.2493254"},{"key":"e_1_3_2_2_16_1","volume-title":"The pathway Coexpression network: revealing pathway relationships. PLoS computational biology","author":"Pita-Juarez Yered","year":"2018","unstructured":"Yered Pita-Juarez , Gabriel Altschuler , Sokratis Kariotis , Wenbin Wei , Katjusa Koler , Claire Green , Rudolph Tanzi , and Winston Hide . 2018. The pathway Coexpression network: revealing pathway relationships. PLoS computational biology , Vol. 14 , 3 ( 2018 ), e1006042. Yered Pita-Juarez, Gabriel Altschuler, Sokratis Kariotis, Wenbin Wei, Katjusa Koler, Claire Green, Rudolph Tanzi, and Winston Hide. 2018. The pathway Coexpression network: revealing pathway relationships. PLoS computational biology , Vol. 14, 3 (2018), e1006042."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882948"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-EJS534"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti062"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2728167"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv683"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-17-0719.1"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3183726"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1198\/106186006X113430"}],"event":{"name":"SIGMOD\/PODS '21: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Virtual Event China","acronym":"SIGMOD\/PODS '21"},"container-title":["Proceedings of the 2021 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457327","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3457327","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448016.3457327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:25:04Z","timestamp":1750195504000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448016.3457327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":24,"alternative-id":["10.1145\/3448016.3457327","10.1145\/3448016"],"URL":"https:\/\/doi.org\/10.1145\/3448016.3457327","relation":{},"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"2021-06-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}