{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:59:49Z","timestamp":1775815189731,"version":"3.50.1"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021ZD0110303"],"award-info":[{"award-number":["2021ZD0110303"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62141220"],"award-info":[{"award-number":["62141220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972253"],"award-info":[{"award-number":["61972253"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1908212"],"award-info":[{"award-number":["U1908212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172276"],"award-info":[{"award-number":["62172276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972254"],"award-info":[{"award-number":["61972254"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Institutions of Higher Learning"},{"name":"Shanghai Science and Technology Development Funds","award":["23YF1420500"],"award-info":[{"award-number":["23YF1420500"]}]},{"name":"Zhejiang Laboratory","award":["2022NL0AB01"],"award-info":[{"award-number":["2022NL0AB01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,12,1]]},"DOI":"10.1109\/tkde.2023.3270750","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T18:50:19Z","timestamp":1682535019000},"page":"12542-12555","source":"Crossref","is-referenced-by-count":20,"title":["<b>COLTR<\/b>: Semi-Supervised Learning to Rank With Co-Training and Over-Parameterization for Web Search"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3869-7881","authenticated-orcid":false,"given":"Yuchen","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-3253","authenticated-orcid":false,"given":"Haoyi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1562-8098","authenticated-orcid":false,"given":"Qingzhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9266-3044","authenticated-orcid":false,"given":"Linghe","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong Province, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1098-4023","authenticated-orcid":false,"given":"Haifang","family":"Li","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6997-1989","authenticated-orcid":false,"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9212-1947","authenticated-orcid":false,"given":"Shuaiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6934-1685","authenticated-orcid":false,"given":"Guihai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2949-6874","authenticated-orcid":false,"given":"Dejing","family":"Dou","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0684-6205","authenticated-orcid":false,"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu, Inc., Beijing, China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.130"},{"key":"ref57","first-page":"327","article-title":"Enhancing supervised learning with unlabeled data","author":"goldman","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01548-9"},{"key":"ref56","first-page":"360","article-title":"Bootstrapping","author":"abney","year":"2002","journal-title":"Proc Annual Meeting of the Assoc Computational Linguistics"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015360"},{"key":"ref59","article-title":"ERNIE: Enhanced representation through knowledge integration","author":"sun","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2200\/S00590ED1V01Y201408AIM029"},{"key":"ref58","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2019","journal-title":"Proc Annu Conf North Amer Chapter Assoc Comput Linguistics"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref52","first-page":"1177","article-title":"Random features for large-scale kernel machines","author":"rahimi","year":"2007","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","article-title":"Semi-supervised learning literature survey","author":"zhu","year":"2005"},{"key":"ref55","first-page":"100","article-title":"Unsupervised models for named entity classification","author":"collins","year":"1999","journal-title":"Proc Joint SIGDAT Conf Empir Methods Natural Lang Process Very Large Corpora"},{"key":"ref10","first-page":"1","article-title":"From theories to queries: Active learning in practice","author":"settles","year":"2011","journal-title":"Proc Act Learn Exp Des Workshop Conjunction AISTATS JMLR Workshop"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277792"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3084140"},{"key":"ref18","first-page":"3146","article-title":"LightGBM: A highly efficient gradient boosting decision tree","author":"ke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401333"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063620"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-06122-3"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462917"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401299"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441751"},{"key":"ref42","first-page":"5587","article-title":"Learning to rank for active learning: A listwise approach","author":"li","year":"2020","journal-title":"Proc IEEE 25th Int Conf Pattern Recognit"},{"key":"ref41","article-title":"Algorithmic foundation of deep X-risk optimization","author":"yang","year":"2022","journal-title":"arXiv 2206 00439"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-82136-4_4"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450078"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531837"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539058"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403297"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/HCS49909.2020.9220641"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467147"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539128"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539080"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331347"},{"key":"ref35","first-page":"65","article-title":"McRank: Learning to rank using multiple classification and gradient boosting","author":"li","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/133160.133199"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273513"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341544"},{"key":"ref31","first-page":"1","article-title":"Co-trained ensemble models for weakly supervised cyberbullying detection","author":"raisi","year":"2017","journal-title":"Proc NIPS Workshop Learn Limited Labeled Data"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-009-0209-z"},{"key":"ref30","first-page":"265","article-title":"Bayesian ensemble learning","author":"chipman","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref74","first-page":"1718","article-title":"Feature learning and random features in standard finite-width convolutional neural networks: An empirical study","author":"samarin","year":"2022","journal-title":"Proc Conf Uncertainty of Artificial Intelligence"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492921000039"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00195"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1903070116"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733078"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531986"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539158"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3130348.3130374"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102363"},{"key":"ref71","first-page":"1","article-title":"On the universality of the double descent peak in ridgeless regression","author":"holzm\u00fcller","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref70","first-page":"13939","article-title":"A random matrix analysis of random fourier features: Beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent","author":"liao","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref73","first-page":"476","article-title":"Nystr&#x00F6;m method vs random fourier features: A theoretical and empirical comparison","author":"yang","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref72","first-page":"1144","article-title":"Optimal rates for random fourier features","author":"sriperumbudur","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775067"},{"key":"ref68","first-page":"1","article-title":"Deep double descent: Where bigger models and more data hurt","author":"nakkiran","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref23","first-page":"1","article-title":"Yahoo! Learning to rank challenge overview","author":"chapelle","year":"2011","journal-title":"Proc Learn to Rank Challenge"},{"key":"ref67","first-page":"21605","article-title":"Model, sample, and epoch-wise descents: Exact solution of gradient flow in the random feature model","author":"bodin","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","first-page":"908","article-title":"Semi-supervised regression with co-training","author":"zhou","year":"2005","journal-title":"Proc 19th Int Joint Conf Artif Intell"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467149"},{"key":"ref69","first-page":"3905","article-title":"Towards a unified analysis of random fourier features","author":"li","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","first-page":"1","article-title":"Are neural rankers still outperformed by gradient boosted decision trees?","author":"qin","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOS1849"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449794"},{"key":"ref22","article-title":"Introducing LETOR 4.0 datasets","author":"qin","year":"2013"},{"key":"ref66","article-title":"On the power and limitations of random features for understanding neural networks","author":"yehudai","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-009-9123-y"},{"key":"ref65","author":"vapnik","year":"1999","journal-title":"The Nature of Statistical Learning Theory"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/11815921_57"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1967-3_8"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071"},{"key":"ref62","first-page":"193","article-title":"Learning to rank with nonsmooth cost functions","author":"burges","year":"2006","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref61","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10311056\/10109140.pdf?arnumber=10109140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T20:55:37Z","timestamp":1702328137000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10109140\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,1]]},"references-count":78,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3270750","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,1]]}}}