{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:31:14Z","timestamp":1775838674937,"version":"3.50.1"},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076138"],"award-info":[{"award-number":["62076138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tnnls.2024.3402108","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T17:37:14Z","timestamp":1717177034000},"page":"6148-6158","source":"Crossref","is-referenced-by-count":3,"title":["Stagewise Training With Exponentially Growing Training Sets"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7165-3143","authenticated-orcid":false,"given":"Bin","family":"Gu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7438-3185","authenticated-orcid":false,"given":"Hilal","family":"AlQuabeh","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8234-6528","authenticated-orcid":false,"given":"William","family":"de Vazelhes","sequence":"additional","affiliation":[{"name":"Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouyuan","family":"Huo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3483-8333","authenticated-orcid":false,"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland, College Park, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1463","article-title":"Starting small-learning with adaptive sample sizes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Daneshmand"},{"key":"ref2","first-page":"4062","article-title":"Adaptive Newton method for empirical risk minimization to statistical accuracy","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mokhtari"},{"key":"ref3","first-page":"2060","article-title":"First-order adaptive sample size methods to reduce complexity of empirical risk minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mokhtari"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3009047"},{"issue":"10","key":"ref5","volume-title":"The Elements of Statistical Learning","volume":"1","author":"Friedman","year":"2001"},{"key":"ref6","first-page":"209","article-title":"Generalized arc consistency for global cardinality constraint","volume-title":"Proc. 13th Nat. Conf. Artif. Intell.","volume":"1","author":"R\u00e9gin"},{"key":"ref7","first-page":"127","article-title":"Gradient hard thresholding pursuit for sparsity-constrained optimization","volume-title":"Proc. 31st Int. Conf. Mach. Learn. (ICML)","author":"Yuan"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3087805"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1137\/140961791"},{"key":"ref11","first-page":"1574","article-title":"Stochastic proximal gradient descent with acceleration techniques","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nitanda"},{"key":"ref12","first-page":"1646","article-title":"SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Defazio"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3201711"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3025383"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1051\/ps:2005018"},{"key":"ref16","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"1999"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000000907"},{"issue":"1","key":"ref18","first-page":"2489","article-title":"Beyond the regret minimization barrier: Optimal algorithms for stochastic strongly-convex optimization","volume":"15","author":"Hazan","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-010-0434-y"},{"key":"ref20","first-page":"1","article-title":"Asynchronous doubly stochastic group regularized learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Gu"},{"key":"ref21","first-page":"1080","article-title":"Improved SVRG for non-strongly-convex or sum-of-non-convex objectives","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Allen-Zhu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2017.2749330"},{"key":"ref23","article-title":"Nonconvex sparse learning via stochastic optimization with progressive variance reduction","author":"Li","year":"2016","journal-title":"arXiv:1605.02711"},{"key":"ref24","article-title":"A tight bound of hard thresholding","author":"Shen","year":"2016","journal-title":"arXiv:1605.01656"},{"key":"ref25","first-page":"1","article-title":"Efficient stochastic gradient hard thresholding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Pan Zhou"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2957109"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2619399"},{"key":"ref28","first-page":"1","article-title":"Accelerated stochastic block coordinate gradient descent for sparsity constrained nonconvex optimization","volume-title":"Proc. UAI","author":"Chen"},{"key":"ref29","first-page":"2305","article-title":"Variance reduced stochastic gradient descent with neighbors","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hofmann"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/10949581\/10542969.pdf?arnumber=10542969","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T05:49:20Z","timestamp":1743832160000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542969\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":29,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3402108","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}