{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T02:30:01Z","timestamp":1784255401129,"version":"3.55.0"},"reference-count":79,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Fundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Project Crossmodal Learning DFG TRR-169"},{"name":"Beijing Academy of Artificial Intelligence"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1565596"],"award-info":[{"award-number":["IIS-1565596"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["III-1615597"],"award-info":[{"award-number":["III-1615597"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1650723"],"award-info":[{"award-number":["IIS-1650723"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-14-1-0631"],"award-info":[{"award-number":["N00014-14-1-0631"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R00LM011392"],"award-info":[{"award-number":["R00LM011392"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21LM012060"],"award-info":[{"award-number":["R21LM012060"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,2,1]]},"DOI":"10.1109\/tpami.2020.3015859","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T22:27:21Z","timestamp":1597184841000},"page":"1002-1019","source":"Crossref","is-referenced-by-count":15,"title":["Model-Protected Multi-Task Learning"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5352-0278","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4336-6777","authenticated-orcid":false,"given":"Jiayu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8088-367X","authenticated-orcid":false,"given":"Changshui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref2","first-page":"196","article-title":"Fast and robust compressive summarization with dual decomposition and multi-task learning","volume-title":"Proc. 51st Annu. Meeting Assoc. Comput. Linguistics","author":"Almeida"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273499"},{"key":"ref4","first-page":"1817","article-title":"A framework for learning predictive structures from multiple tasks and unlabeled data","volume":"6","author":"Ando","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0010"},{"key":"ref6","first-page":"25","article-title":"A spectral regularization framework for multi-task structure learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Argyriou"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5040-8"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1265530.1265569"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2012.67"},{"key":"ref11","first-page":"153","article-title":"Multi-task gaussian process prediction","volume-title":"Proc. Neural Inf. Process. Syst.","volume":"20","author":"Bonilla"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2043621.2043626"},{"key":"ref14","first-page":"289","article-title":"Privacy-preserving logistic regression","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chaudhuri"},{"key":"ref15","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref16","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"Chaudhuri","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553392"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020423"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2086737.2086742"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/1806689.1806787"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2591796.2591883"},{"key":"ref24","first-page":"41","article-title":"Multi-task feature learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","volume":"19","author":"Evgeniou"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-012-0543-4"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4399"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401926"},{"key":"ref28","article-title":"Secure multi-party computation","author":"Goldreich","year":"1998"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339672"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2009.32"},{"key":"ref31","first-page":"600","article-title":"Low-rank and sparse structure pursuit via alternating minimization","volume-title":"Proc. 19th Int. Conf. Artif. Intell. Statist.","volume":"51","author":"Gu"},{"key":"ref32","volume-title":"Matrix Variate Distributions","volume":"104","author":"Gupta","year":"1999"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31863-9_8"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10261"},{"key":"ref35","volume-title":"Nonparametric Statistical Methods.","volume":"751","author":"Hollander","year":"2013"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2488608.2488651"},{"key":"ref37","first-page":"427","article-title":"Revisiting frank-wolfe: Projection-free sparse convex optimization","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Jaggi"},{"key":"ref38","first-page":"964","article-title":"A dirty model for multi-task learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Jalali"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553434"},{"key":"ref40","article-title":"Select and label (S & L): A task-driven privacy-preserving data synthesization framework","volume-title":"Proc. Translational Bioinf. Conf.","author":"Ji"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10185"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2685505"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1137\/090756090"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/2554797.2554834"},{"key":"ref45","article-title":"Robust mediators in large games,","author":"Kearns"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"ref47","first-page":"475","article-title":"Differentially private synthesization of multi-dimensional data using copula functions","volume-title":"Proc. 17th Int. Conf. Extending Database Technol.","author":"Li"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-010-0354-6"},{"key":"ref49","first-page":"339","article-title":"Multi-task feature learning via efficient l 2, 1-norm minimization","volume-title":"Proc. 25th Conf. Uncertainty Artif. Intell.","author":"Liu"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2012.180"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref52","article-title":"Fast feature fool: A data independent approach to universal adversarial perturbations,","author":"Mopuri"},{"key":"ref53","first-page":"132","article-title":"Personalized predictive modeling and risk factor identification using patient similarity,","volume-title":"Proc. AMIA Summits Translational Sci.","author":"Ng"},{"key":"ref54","first-page":"1813","article-title":"Efficient and robust feature selection via joint \u21132, 1-norms minimization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Nie"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/1250790.1250803"},{"key":"ref56","article-title":"Transferability in machine learning: From phenomena to black-box attacks using adversarial samples","author":"Papernot","year":"2016"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref58","first-page":"1876","article-title":"Multiparty differential privacy via aggregation of locally trained classifiers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Pathak"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1137\/090763184"},{"key":"ref60","first-page":"1458","article-title":"Convergence rates of inexact proximal-gradient methods for convex optimization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Schmidt"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2679002"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783324"},{"key":"ref65","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2014"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1198\/004017005000000139"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57048-8_7"},{"key":"ref68","first-page":"1180","article-title":"Exploring joint disease risk prediction","volume-title":"Proc. AMIA Annu. Symp.","volume":"2014","author":"Wang"},{"key":"ref69","first-page":"2493","article-title":"Privacy for free: Posterior sampling and stochastic gradient monte carlo","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2009.2016892"},{"key":"ref71","first-page":"1479","article-title":"Conditional sparse coding and grouped multivariate regression","volume-title":"Proc. 29th Int. Conf. Mach. Learn","author":"Xu"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140449"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350253"},{"key":"ref74","first-page":"132","article-title":"Towards personalized medicine: leveraging patient similarity and drug similarity analytics","volume-title":"Proc. AMIA Summits Translational Sci.","author":"Zhang"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247908"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-b1155168"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2688363"},{"key":"ref79","first-page":"702","article-title":"Clustered multi-task learning via alternating structure optimization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/9674188\/9165155-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9674188\/09165155.pdf?arnumber=9165155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:23:22Z","timestamp":1704842602000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9165155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":79,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3015859","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}