{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T01:01:28Z","timestamp":1767142888197,"version":"build-2238731810"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:00:00Z","timestamp":1684454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Army Research Office Early Career Award"},{"name":"National Science Foundation","award":["IIS-183817"],"award-info":[{"award-number":["IIS-183817"]}]},{"name":"National Science Foundation","award":["ECCS-2037304"],"award-info":[{"award-number":["ECCS-2037304"]}]},{"name":"National Science Foundation","award":["DMS-2134248"],"award-info":[{"award-number":["DMS-2134248"]}]},{"name":"ACCESS \u2013 AI Chip Center for Emerging Smart Systems, sponsored by InnoHK funding"},{"name":"Hong Kong SAR"},{"name":"Precourt Institute seed grant"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11227-023-05309-w","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:01:48Z","timestamp":1684454508000},"page":"18748-18776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sketching the Krylov subspace: faster computation of the entire ridge regularization path"],"prefix":"10.1007","volume":"79","author":[{"given":"Yifei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mert","family":"Pilanci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,19]]},"reference":[{"issue":"2","key":"5309_CR1","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/00401706.1979.10489751","volume":"21","author":"GH Golub","year":"1979","unstructured":"Golub GH, Heath M, Wahba G (1979) Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2):215\u2013223","journal-title":"Technometrics"},{"key":"5309_CR2","first-page":"1352","volume":"1","author":"K-C Li","year":"1985","unstructured":"Li K-C (1985) From Stein\u2019s unbiased risk estimates to the method of generalized cross validation. Ann Statist 1:1352\u20131377","journal-title":"Ann Statist"},{"key":"5309_CR3","doi-asserted-by":"crossref","unstructured":"Vogel CR (2002) Computational methods for inverse problems","DOI":"10.1137\/1.9780898717570"},{"key":"5309_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.csda.2013.06.006","volume":"68","author":"P Exterkate","year":"2013","unstructured":"Exterkate P (2013) Model selection in kernel ridge regression. Computat Statist Data Anal 68:1\u201316","journal-title":"Computat Statist Data Anal"},{"key":"5309_CR5","doi-asserted-by":"crossref","unstructured":"Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International Conference on Artificial Neural Networks. Springer pp 270\u2013279","DOI":"10.1007\/978-3-030-01424-7_27"},{"issue":"1","key":"5309_CR6","first-page":"1842","volume":"17","author":"M Pilanci","year":"2016","unstructured":"Pilanci M, Wainwright MJ (2016) Iterative hessian sketch: fast and accurate solution approximation for constrained least-squares. J Mach Learn Res 17(1):1842\u20131879","journal-title":"J Mach Learn Res"},{"issue":"12","key":"5309_CR7","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1093\/gigascience\/giaa133","volume":"9","author":"A Rokem","year":"2020","unstructured":"Rokem A, Kay K (2020) Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. GigaScience 9(12):133","journal-title":"GigaScience"},{"key":"5309_CR8","unstructured":"Rudi A, Camoriano R, Rosasco L (2015) Less is More: Nystr\u00f6m computational regularization. In: NIPS, pp. 1657\u20131665"},{"key":"5309_CR9","doi-asserted-by":"crossref","unstructured":"Hackbusch W (1994) Iterative solution of large sparse systems of equations 95","DOI":"10.1007\/978-1-4612-4288-8"},{"issue":"36","key":"5309_CR10","doi-asserted-by":"publisher","first-page":"13212","DOI":"10.1073\/pnas.0804869105","volume":"105","author":"V Rokhlin","year":"2008","unstructured":"Rokhlin V, Tygert M (2008) A fast randomized algorithm for overdetermined linear least-squares regression. Proc Natl Acad Sci 105(36):13212\u201313217","journal-title":"Proc Natl Acad Sci"},{"key":"5309_CR11","unstructured":"Woodruff DP (2014) Sketching as a tool for numerical linear algebra. arXiv preprint arXiv:1411.4357"},{"issue":"1","key":"5309_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2559902","volume":"61","author":"DM Kane","year":"2014","unstructured":"Kane DM, Nelson J (2014) Sparser johnson-lindenstrauss transforms. J ACM (JACM) 61(1):1\u201323","journal-title":"J ACM (JACM)"},{"key":"5309_CR13","doi-asserted-by":"crossref","unstructured":"Ailon N, Chazelle B (2006) Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform. In: Proceedings of the Thirty-eighth Annual ACM Symposium on Theory of Computing, pp. 557\u2013563","DOI":"10.1145\/1132516.1132597"},{"key":"5309_CR14","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/0024-3795(84)90221-0","volume":"58","author":"A Ruhe","year":"1984","unstructured":"Ruhe A (1984) Rational krylov sequence methods for eigenvalue computation. Linear Algebra Appl 58:391\u2013405","journal-title":"Linear Algebra Appl"},{"key":"5309_CR15","first-page":"19377","volume":"33","author":"J Lacotte","year":"2020","unstructured":"Lacotte J, Pilanci M (2020) Effective dimension adaptive sketching methods for faster regularized least-squares optimization. Adv Neural Inform Process Syst 33:19377\u201319387","journal-title":"Adv Neural Inform Process Syst"},{"key":"5309_CR16","doi-asserted-by":"crossref","unstructured":"Ozaslan IK, Pilanci M, Arikan O (2020) Regularized momentum iterative hessian sketch for large scale linear system of equations. International Conferene on Acoustics, Speech and Signal Processing","DOI":"10.1109\/ICASSP.2019.8682720"},{"key":"5309_CR17","unstructured":"Avron H, Clarkson KL, Woodruff DP (2017) Sharper bounds for regularized data fitting. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX\/RANDOM 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik"},{"issue":"3","key":"5309_CR18","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1137\/21M1422963","volume":"43","author":"N Gazagnadou","year":"2022","unstructured":"Gazagnadou N, Ibrahim M, Gower RM (2022) Ridgesketch: a fast sketching based solver for large scale ridge regression. SIAM J Matrix Anal Appl 43(3):1440\u20131468","journal-title":"SIAM J Matrix Anal Appl"}],"updated-by":[{"DOI":"10.1007\/s11227-023-05476-w","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000}}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05309-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-023-05309-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-023-05309-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T04:13:06Z","timestamp":1695010386000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-023-05309-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,19]]},"references-count":18,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["5309"],"URL":"https:\/\/doi.org\/10.1007\/s11227-023-05309-w","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,19]]},"assertion":[{"value":"14 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2023","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11227-023-05476-w","URL":"https:\/\/doi.org\/10.1007\/s11227-023-05476-w","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}