{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:51:58Z","timestamp":1789753918778,"version":"4.0.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1947203, IIS-2117902, IIS-2137468"],"award-info":[{"award-number":["IIS-1947203, IIS-2117902, IIS-2137468"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539311","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"2594-2604","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Contrastive Learning with Complex Heterogeneity"],"prefix":"10.1145","author":[{"given":"Lecheng","family":"Zheng","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjun","family":"Xiong","sequence":"additional","affiliation":[{"name":"University at Buffalo, Buffalo, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yada","family":"Zhu","sequence":"additional","affiliation":[{"name":"MIT-IBM Watson AI Lab, IBM Research, Boston, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingrui","family":"He","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Champaign, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"A kernel method for canonical correlation analysis. CoRR abs\/cs\/0609071","author":"Akaho Shotaro","year":"2006","unstructured":"Shotaro Akaho. 2006. A kernel method for canonical correlation analysis. CoRR abs\/cs\/0609071 (2006)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-016-9556-4"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","volume":"119","author":"Chen Ting","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, Vol. 119. PMLR, 1597--1607."},{"key":"e_1_3_2_2_6_1","volume-title":"Debiased Contrastive Learning. In Advances in Annual Conference on Neural Information Processing Systems","author":"Chuang Ching-Yao","year":"2020","unstructured":"Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. 2020. Debiased Contrastive Learning. In Advances in Annual Conference on Neural Information Processing Systems 2020."},{"key":"e_1_3_2_2_7_1","unstructured":"Marco Federici Anjan Dutta Patrick Forr\u00e9 Nate Kushman and Zeynep Akata. 2020. Learning Robust Representations via Multi-View Information Bottleneck. (2020)."},{"key":"e_1_3_2_2_8_1","volume-title":"Adversarial Graph Contrastive Learning with Information Regularization. In WWW '22: The ACM Web Conference","author":"Feng Shengyu","year":"2022","unstructured":"Shengyu Feng, Baoyu Jing, Yada Zhu, and Hanghang Tong. 2022. Adversarial Graph Contrastive Learning with Information Regularization. In WWW '22: The ACM Web Conference 2022. ACM, 1362--1371."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412127"},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of the 28th International Conference on Machine Learning, ICML 2011. Omnipress, 25--32","author":"He Jingrui","year":"2011","unstructured":"Jingrui He and Rick Lawrence. 2011. A Graphbased Framework for Multi-Task Multi-View Learning. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011. Omnipress, 25--32."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.86"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2892753.2892812"},{"key":"e_1_3_2_2_13_1","volume-title":"Heterogeneous contrastive learning: Encoding spatial information for compact visual representations. arXiv preprint arXiv:2011.09941","author":"Huo Xinyue","year":"2020","unstructured":"Xinyue Huo, Lingxi Xie, Longhui Wei, Xiaopeng Zhang, Hao Li, Zijie Yang, Wengang Zhou, Houqiang Li, and Qi Tian. 2020. Heterogeneous contrastive learning: Encoding spatial information for compact visual representations. arXiv preprint arXiv:2011.09941 (2020)."},{"key":"e_1_3_2_2_14_1","volume-title":"HDMI: High-order Deep Multiplex Infomax. In WWW '21: The Web Conference","author":"Jing Baoyu","year":"2021","unstructured":"Baoyu Jing, Chanyoung Park, and Hanghang Tong. 2021. HDMI: High-order Deep Multiplex Infomax. In WWW '21: The Web Conference 2021. ACM \/ IW3C2, 2414--2424."},{"key":"e_1_3_2_2_15_1","volume-title":"Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs. arXiv preprint arXiv:2109.03560","author":"Jing Baoyu","year":"2021","unstructured":"Baoyu Jing, Yuejia Xiang, Xi Chen, Yu Chen, and Hanghang Tong. 2021. Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs. arXiv preprint arXiv:2109.03560 (2021)."},{"key":"e_1_3_2_2_16_1","volume-title":"Supervised Contrastive Learning. In Advances in Annual Conference on Neural Information Processing Systems","author":"Khosla Prannay","year":"2020","unstructured":"Prannay Khosla, Piotr Teterwak, ChenWang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised Contrastive Learning. In Advances in Annual Conference on Neural Information Processing Systems 2020."},{"key":"e_1_3_2_2_17_1","volume-title":"Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. In Proceedings of the 27th International Conference on Machine Learning","author":"Kim Seyoung","year":"2010","unstructured":"Seyoung Kim and Eric P. Xing. 2010. Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. In Proceedings of the 27th International Conference on Machine Learning, 2010. Omnipress, 543--550."},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the Nineteenth International Conference (ICML","author":"Lanckriet Gert R. G.","year":"2002","unstructured":"Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, and Michael I. Jordan. 2002. Learning the Kernel Matrix with Semi-Definite Programming. In Proceedings of the Nineteenth International Conference (ICML 2002). Morgan Kaufmann, 323--330."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_20_1","volume-title":"Graph Communal Contrastive Learning. In WWW '22: The ACM Web Conference","author":"Li Bolian","year":"2022","unstructured":"Bolian Li, Baoyu Jing, and Hanghang Tong. 2022. Graph Communal Contrastive Learning. In WWW '22: The ACM Web Conference 2022. ACM, 1203--1213."},{"key":"e_1_3_2_2_21_1","volume-title":"Deep Learning Face Attributes in the Wild. In 2015 IEEE International Conference on Computer Vision, ICCV","author":"Liu Ziwei","year":"2015","unstructured":"Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep Learning Face Attributes in the Wild. In 2015 IEEE International Conference on Computer Vision, ICCV 2015. IEEE Computer Society, 3730--3738."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.126"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/2891460.2891550"},{"key":"e_1_3_2_2_24_1","volume-title":"Yuille","author":"Mao Junhua","year":"2014","unstructured":"Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, and Alan L. Yuille. 2014. Explain Images with Multimodal Recurrent Neural Networks. CoRR abs\/1410.1090 (2014)."},{"key":"e_1_3_2_2_25_1","volume-title":"Cross-Stitch Networks for Multi-task Learning. In 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 3994--4003","author":"Misra Ishan","year":"2016","unstructured":"Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-Stitch Networks for Multi-task Learning. In 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 3994--4003."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2754939"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.02.045"},{"key":"e_1_3_2_2_29_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015."},{"key":"e_1_3_2_2_30_1","volume-title":"Improved Deep Metric Learning with Multi-class N-pair Loss Objective. In Advances in Annual Conference on Neural Information Processing Systems 2016. 1849","author":"Sohn Kihyuk","year":"2016","unstructured":"Kihyuk Sohn. 2016. Improved Deep Metric Learning with Multi-class N-pair Loss Objective. In Advances in Annual Conference on Neural Information Processing Systems 2016. 1849--1857."},{"key":"e_1_3_2_2_31_1","volume-title":"Multi-label Contrastive Predictive Coding. In Advances in Annual Conference on Neural Information Processing Systems","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song and Stefano Ermon. 2020. Multi-label Contrastive Predictive Coding. In Advances in Annual Conference on Neural Information Processing Systems 2020."},{"key":"e_1_3_2_2_32_1","volume-title":"Computer Vision - ECCV 2020 - 16th European Conference","author":"Tian Yonglong","unstructured":"Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive Multiview Coding. In Computer Vision - ECCV 2020 - 16th European Conference, Vol. 12356. Springer, 776--794."},{"key":"e_1_3_2_2_33_1","volume-title":"Representation Learning with Contrastive Predictive Coding. CoRR abs\/1807.03748","author":"van den Oord A\u00e4ron","year":"2018","unstructured":"A\u00e4ron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. CoRR abs\/1807.03748 (2018)."},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning, ICML 2015","volume":"37","author":"Wang Weiran","unstructured":"Weiran Wang, Raman Arora, Karen Livescu, and Jeff A. Bilmes. 2015. On Deep Multi-View Representation Learning. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Vol. 37. JMLR.org, 1083--1092."},{"key":"e_1_3_2_2_35_1","volume-title":"X-ray microbeam speech production database user's handbook: Madison","author":"Westbury JR","year":"1994","unstructured":"JR Westbury. 1994. X-ray microbeam speech production database user's handbook: Madison. WI: Waisman Center, University of Wisconsin (1994)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467214"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2417578"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2490539"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939798"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0781-x"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298647"},{"key":"e_1_3_2_2_42_1","volume-title":"Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888","author":"You Yang","year":"2017","unstructured":"Yang You, Igor Gitman, and Boris Ginsburg. 2017. Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888 (2017)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835930"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.2"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449801"},{"key":"e_1_3_2_2_46_1","volume-title":"Tackling Oversmoothing of GNNs with Contrastive Learning. arXiv preprint arXiv:2110.13798","author":"Zheng Lecheng","year":"2021","unstructured":"Lecheng Zheng, Dongqi Fu, and Jingrui He. 2021. Tackling Oversmoothing of GNNs with Contrastive Learning. arXiv preprint arXiv:2110.13798 (2021)."},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of the Twenty-Fourth International Conference (ICML","volume":"227","author":"Zhou Dengyong","year":"2007","unstructured":"Dengyong Zhou and Christopher J. C. Burges. 2007. Spectral clustering and transductive learning with multiple views. In Proceedings of the Twenty-Fourth International Conference (ICML 2007), Vol. 227. ACM, 1159--1166."},{"key":"e_1_3_2_2_48_1","volume-title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI","author":"Zhou Dawei","year":"2015","unstructured":"Dawei Zhou, Jingrui He, K. Sel\u00e7uk Candan, and Hasan Davulcu. 2015. MUVIR: Multi-View Rare Category Detection. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015. AAAI Press, 4098--4104."},{"key":"e_1_3_2_2_49_1","volume-title":"Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. In WWW '20: The Web Conference","author":"Zhou Dawei","year":"2020","unstructured":"Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, and Jingrui He. 2020. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. In WWW '20: The Web Conference 2020. ACM \/ IW3C2, 2230--2240."},{"key":"e_1_3_2_2_50_1","volume-title":"Clustered Multi-Task Learning Via Alternating Structure Optimization. In Advances in 25th Annual Conference on Neural Information Processing Systems","author":"Zhou Jiayu","year":"2011","unstructured":"Jiayu Zhou, Jianhui Chen, and Jieping Ye. 2011. Clustered Multi-Task Learning Via Alternating Structure Optimization. In Advances in 25th Annual Conference on Neural Information Processing Systems 2011. 702--710."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2785795"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539311","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539311","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539311","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:47Z","timestamp":1750186967000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":51,"alternative-id":["10.1145\/3534678.3539311","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539311","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}