{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T18:18:29Z","timestamp":1761848309889,"version":"build-2065373602"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306036","72495125"],"award-info":[{"award-number":["62306036","72495125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Innovation Program of Hunan Province","doi-asserted-by":"publisher","award":["2024RC4008","AC2024040911247631FF26"],"award-info":[{"award-number":["2024RC4008","AC2024040911247631FF26"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Program of Sichuan Provincial Social Science Foundation","award":["SCJJ24ZD20"],"award-info":[{"award-number":["SCJJ24ZD20"]}]},{"name":"Xin Shen was supported by the General Program of Chinese Postdoctoral Science Foundation","award":["2024M753431"],"award-info":[{"award-number":["2024M753431"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tnnls.2025.3590131","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T18:44:10Z","timestamp":1753296250000},"page":"19951-19963","source":"Crossref","is-referenced-by-count":0,"title":["Progressive Training for Learning From Label Proportions"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6914-8941","authenticated-orcid":false,"given":"Jiabin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-8185","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technology and Management, University of International Business and Economics, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuping","family":"Zhang","sequence":"additional","affiliation":[{"name":"the School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3260-4119","authenticated-orcid":false,"given":"Huadong","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing ModelBest Intelligent Technology Company Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7574-9265","authenticated-orcid":false,"given":"Xin","family":"Shen","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9220-8647","authenticated-orcid":false,"given":"Gang","family":"Kou","sequence":"additional","affiliation":[{"name":"Xiangjiang Laboratory, School of Digital Media Engineering and Humanities, Hunan University of Technology and Business, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref2","first-page":"703","article-title":"Analysis of learning from positive and unlabeled data","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","author":"Plessis"},{"key":"ref3","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref4","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Sakai"},{"key":"ref5","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"},{"key":"ref6","first-page":"3235","article-title":"Realistic evaluation of deep semi-supervised learning algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Oliver"},{"key":"ref7","first-page":"11091","article-title":"Leveraged weighted loss for partial label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wen"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2721942"},{"key":"ref9","first-page":"6500","article-title":"Progressive identification of true labels for partial-label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lv"},{"key":"ref10","first-page":"1196","article-title":"Learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"26","author":"Natarajan"},{"key":"ref11","first-page":"125","article-title":"Learning from corrupted binary labels via class-probability estimation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Menon"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref13","first-page":"3355","article-title":"Dimensionality-driven learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref14","first-page":"8527","article-title":"Co-teaching: Robust training deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref15","first-page":"961","article-title":"On symmetric losses for learning from corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Charoenphakdee"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401920"},{"key":"ref17","first-page":"1675","article-title":"Positive-unlabeled learning with non-negative risk estimator","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kiryo"},{"key":"ref18","first-page":"5917","article-title":"Binary classification from positive-confidence data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ishida"},{"key":"ref19","first-page":"452","article-title":"Classification from pairwise similarity and unlabeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bao"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3348833"},{"key":"ref21","first-page":"5639","article-title":"Learning from complementary labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Ishida"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3089337"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.08.014"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1177\/0962280216651098"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2017.144"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654935"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654993"},{"key":"ref28","article-title":"Learning about individuals from group statistics","author":"Kuck","year":"2012","journal-title":"arXiv:1207.1393"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/icdm.2017.54"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.288"},{"key":"ref31","first-page":"36","article-title":"Distributed traffic flow prediction with label proportions: From in-network towards high performance computation with MPI","volume-title":"Proc. Int. Conf. Mining Urban Data","author":"Liebig"},{"key":"ref32","first-page":"7167","article-title":"Learning from label proportions with generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref33","first-page":"513","article-title":"Learning from label proportions with consistency regularization","volume-title":"Proc. Asian Conf. Mach. Learn.","author":"Tsai"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20112"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1561\/2200000073"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/377"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108767"},{"key":"ref40","first-page":"1","article-title":"SVM for learning with label proportions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref41","article-title":"Deep multi-class learning from label proportions","author":"Dulac-Arnold","year":"2019","journal-title":"arXiv:1905.12909"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01519"},{"key":"ref43","first-page":"26933","article-title":"Learning from label proportions by learning with label noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref44","first-page":"2292","article-title":"Sinkhorn distances: Lightspeed computation of optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Cuturi"},{"key":"ref45","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref46","article-title":"Learning from label proportions with consistency regularization","author":"Tsai","year":"2019","journal-title":"arXiv:1910.13188"},{"issue":"86","key":"ref47","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11220834\/11091506.pdf?arnumber=11091506","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T18:03:26Z","timestamp":1761847406000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11091506\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":47,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3590131","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}