{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:34:43Z","timestamp":1770888883803,"version":"3.50.1"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1816227"],"award-info":[{"award-number":["IIS-1816227"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["73042CS"],"award-info":[{"award-number":["73042CS"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["61620106003"],"award-info":[{"award-number":["61620106003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0101005"],"award-info":[{"award-number":["2018AAA0101005"]}],"id":[{"id":"10.13039\/501100012166","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":[[2022,1,1]]},"DOI":"10.1109\/tpami.2020.3005393","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T22:22:12Z","timestamp":1593469332000},"page":"76-86","source":"Crossref","is-referenced-by-count":10,"title":["Average Top-k Aggregate Loss for Supervised Learning"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0992-685X","authenticated-orcid":false,"given":"Siwei","family":"Lyu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8530-485X","authenticated-orcid":false,"given":"Yanbo","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7345-6672","authenticated-orcid":false,"given":"Yiming","family":"Ying","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6916-5394","authenticated-orcid":false,"given":"Bao-Gang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000000907"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-004-0134-1"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1233901"},{"key":"ref4","volume-title":"Statistical Learning Theory","volume":"1","author":"Vapnik","year":"1998"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-004-0155-9"},{"key":"ref6","first-page":"793","article-title":"Minimizing the maximal loss: How and why","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Shalev-Shwartz and"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015565"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref10","first-page":"497","article-title":"Learning with average top-k loss","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Fan"},{"key":"ref11","first-page":"2975","article-title":"Variance-based regularization with convex objectives","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Duchi"},{"key":"ref12","first-page":"313","article-title":"AUC optimization vs. error rate minimization","volume-title":"Proc. 16th Int. Conf. Neural Inf. Process. Syst.","author":"Cortes"},{"key":"ref13","volume-title":"Pattern Classification","author":"Duda","year":"2000"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289503"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1971.10482346"},{"key":"ref16","article-title":"Loss functions for binary class probability estimation and classification: Structure and applications","author":"Buja","year":"2005"},{"key":"ref17","first-page":"1049","article-title":"On the design of loss functions for classification: Theory, robustness to outliers, and SavageBoost","volume-title":"Proc. 21st Int. Conf. Neural Inf. Process. Syst.","author":"Masnadi-Shirazi and"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref19","first-page":"2387","article-title":"Composite binary losses","volume":"11","author":"Reid","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.220"},{"key":"ref21","first-page":"1049","article-title":"On the design of loss functions for classification: Theory, robustness to outliers, and SavageBoost","volume-title":"Proc. 21st Int. Conf. Neural Inf. Process. Syst.","author":"Masnadi-Shirazi and"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000617"},{"key":"ref23","first-page":"2532","article-title":"Relaxed clipping: A global training method for robust regression and classification","volume-title":"Proc. 23rd Int. Conf. Neural Inf. Process. Syst.","author":"Yu"},{"key":"ref24","first-page":"2233","article-title":"The P-Norm push: A simple convex ranking algorithm that concentrates at the top of the list","volume":"10","author":"Rudin","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553509"},{"key":"ref26","first-page":"2764","article-title":"WSABIE: Scaling up to large vocabulary image annotation","volume-title":"Proc. Int. Joint Conf. Artif. Intell.","author":"Weston"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1162\/15324430260185628"},{"key":"ref28","first-page":"325","article-title":"Top-k multiclass SVM","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Lapin"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.163"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2751607"},{"key":"ref31","first-page":"1954","article-title":"Submodularity in data subset selection and active learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wei"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref33","first-page":"1189","article-title":"Self-paced learning for latent variable models","volume-title":"Proc. 23rd Int. Conf. Neural Inf. Process. Syst.","author":"Kumar"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.135"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-0190(02)00370-8"},{"key":"ref39","volume-title":"Numerical Optimization","author":"Nocedal","year":"2006"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/BF00939948"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/110840054"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1023\/a:1017501703105"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/8996.003.0015"},{"key":"ref44","first-page":"1571","article-title":"Making gradient descent optimal for strongly convex stochastic optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rakhlin et"},{"key":"ref45","article-title":"Stochastic optimization for machine learning","author":"Srebro","year":"2010","journal-title":"ICML Tut."},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"ref48","volume-title":"Support Vector Machines","author":"Steinwart","year":"2008"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0711-5"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2004.03.002"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.orl.2007.01.001"},{"key":"ref52","article-title":"Concentration of risk measures: A wasserstein distance approach","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Prashanth"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2956775"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3379500"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/9639876\/9127807-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9639876\/09127807.pdf?arnumber=9127807","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:23:57Z","timestamp":1704839037000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9127807\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":54,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3005393","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":[[2022,1,1]]}}}