{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T13:32:54Z","timestamp":1772717574564,"version":"3.50.1"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030864859","type":"print"},{"value":"9783030864866","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86486-6_33","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"538-553","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Multi-task Augmented Feature Learning via Hierarchical Graph Neural Network"],"prefix":"10.1007","author":[{"given":"Pengxin","family":"Guo","sequence":"first","affiliation":[]},{"given":"Chang","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Linjie","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaonan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"33_CR1","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, pp. 265\u2013283 (2016)"},{"key":"33_CR2","first-page":"1817","volume":"6","author":"RK Ando","year":"2005","unstructured":"Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6, 1817\u20131853 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR3","first-page":"41","volume":"19","author":"A Argyriou","year":"2006","unstructured":"Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. Adv. Neural. Inf. Process. Syst. 19, 41\u201348 (2006)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR4","first-page":"463","volume":"3","author":"PL Bartlett","year":"2002","unstructured":"Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: Risk bounds and structural results. J. Mach. Learn. Res. 3, 463\u2013482 (2002)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Bickel, S., Bogojeska, J., Lengauer, T., Scheffer, T.: Multi-task learning for HIV therapy screening. In: Proceedings of the Twenty-Fifth International Conference on Machine Learning, pp. 56\u201363 (2008)","DOI":"10.1145\/1390156.1390164"},{"key":"33_CR6","unstructured":"Bonilla, E., Chai, K.M.A., Williams, C.: Multi-task Gaussian process prediction. In: Advances in Neural Information Processing Systems 20, pp. 153\u2013160. Vancouver, British Columbia, Canada (2007)"},{"key":"33_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-319-11382-1_18","volume-title":"Information Access Evaluation. Multilinguality, Multimodality, and Interaction","author":"B Caputo","year":"2014","unstructured":"Caputo, B., et al.: ImageCLEF 2014: overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 192\u2013211. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11382-1_18"},{"issue":"1","key":"33_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41\u201375 (1997)","journal-title":"Mach. Learn."},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Gao, Y., Bai, H., Jie, Z., Ma, J., Jia, K., Liu, W.: MTL-NAS: task-agnostic neural architecture search towards general-purpose multi-task learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.01156"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066\u20132073. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Han, L., Zhang, Y.: Learning multi-level task groups in multi-task learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9581"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Han, L., Zhang, Y.: Multi-stage multi-task learning with reduced rank. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10261"},{"key":"33_CR13","first-page":"745","volume":"21","author":"L Jacob","year":"2008","unstructured":"Jacob, L., Bach, F., Vert, J.P.: Clustered multi-task learning: a convex formulation. Adv. Neural. Inf. Process. Syst. 21, 745\u2013752 (2008)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR14","unstructured":"Jalali, A., Ravikumar, P.D., Sanghavi, S., Ruan, C.: A dirty model for multi-task learning. In: Advances in Neural Information Processing Systems 23, pp. 964\u2013972. Vancouver, British Columbia, Canada (2010)"},{"key":"33_CR15","unstructured":"Kim, R., So, C.H., Jeong, M., Lee, S., Kim, J., Kang, J.: HATS: A hierarchical graph attention network for stock movement prediction. CoRR abs\/1908.07999 (2019)"},{"key":"33_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR17","unstructured":"Kumar, A., III, H.D.: Learning task grouping and overlap in multi-task learning. In: Proceedings of the 29th International Conference on Machine Learning. Edinburgh, Scotland, UK (2012)"},{"issue":"1","key":"33_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s11263-014-0767-8","volume":"113","author":"S Li","year":"2015","unstructured":"Li, S., Liu, Z., Chan, A.B.: Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network. IJCV 113(1), 19\u201336 (2015)","journal-title":"IJCV"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Liu, H., Palatucci, M., Zhang, J.: Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. In: Proceedings of the 26th Annual International Conference on Machine Learning (2009)","DOI":"10.1145\/1553374.1553458"},{"key":"33_CR20","unstructured":"Liu, P., Fu, J., Dong, Y., Qiu, X., Cheung, J.C.K.: Multi-task learning over graph structures. CoRR abs\/1811.10211 (2018)"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Liu, S., Johns, E., Davison, A.J.: End-to-end multi-task learning with attention. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1871\u20131880 (2019)","DOI":"10.1109\/CVPR.2019.00197"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3707\u20133715 (2015)","DOI":"10.1109\/CVPR.2015.7298994"},{"key":"33_CR23","unstructured":"Lozano, A.C., Swirszcz, G.: Multi-level lasso for sparse multi-task regression. In: Proceedings of the 29th International Conference on Machine Learning. Edinburgh, Scotland, UK (2012)"},{"key":"33_CR24","unstructured":"Lu, H., Huang, S.H., Ye, T., Guo, X.: Graph star net for generalized multi-task learning. CoRR abs\/1906.12330 (2019)"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Meng, Z., Adluru, N., Kim, H.J., Fung, G., Singh, V.: Efficient relative attribute learning using graph neural networks. In: Proceedings of the 15th European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01264-9_34"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3994\u20134003 (2016)","DOI":"10.1109\/CVPR.2016.433"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Mrksic, N., et al.: Multi-domain dialog state tracking using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 794\u2013799 (2015)","DOI":"10.3115\/v1\/P15-2130"},{"key":"33_CR28","unstructured":"Obozinski, G., Taskar, B., Jordan, M.: Multi-task feature selection. Technical report,Department of Statistics, University of California, Berkeley (June 2006)"},{"issue":"3","key":"33_CR29","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"33_CR30","unstructured":"Ryu, H., Shin, H., Park, J.: Multi-agent actor-critic with hierarchical graph attention network. CoRR abs\/1909.12557 (2019)"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Shinohara, Y.: Adversarial multi-task learning of deep neural networks for robust speech recognition. In: Proceedings of the 17th Annual Conference of the International Speech Communication Association, pp. 2369\u20132372 (2016)","DOI":"10.21437\/Interspeech.2016-879"},{"key":"33_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"33_CR33","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep hashing network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5018\u20135027 (2017)","DOI":"10.1109\/CVPR.2017.572"},{"key":"33_CR35","unstructured":"Yang, Y., Hospedales, T.M.: Deep multi-task representation learning: a tensor factorisation approach. In: Proceedings of the 6th International Conference on Learning Representations (2017)"},{"key":"33_CR36","unstructured":"Yang, Y., Hospedales, T.M.: Trace norm regularised deep multi-task learning. In: Proceedings of the 6th International Conference on Learning Representations, Workshop Track (2017)"},{"key":"33_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, W., et al.: Deep model based transfer and multi-task learning for biological image analysis. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1475\u20131484 (2015)","DOI":"10.1145\/2783258.2783304"},{"key":"33_CR38","unstructured":"Zhang, Y., Yang, Q.: A survey on multi-task learning. CoRR abs\/1707.08114 (2017)"},{"key":"33_CR39","unstructured":"Zhang, Y., Yeung, D.Y.: A convex formulation for learning task relationships in multi-task learning. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pp. 733\u2013742 (2010)"},{"key":"33_CR40","unstructured":"Zhang, Y., Wei, Y., Yang, Q.: Learning to multitask. In: Advances in NeuralInformation Processing Systems 31, pp. 5776\u20135787 (2018)"},{"key":"33_CR41","unstructured":"Zhang, Y., Yeung, D., Xu, Q.: Probabilistic multi-task feature selection. Probabilistic multi-task feature selection. In: Advancesin Neural Information Processing Systems 23, pp. 2559\u20132567 (2010)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86486-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:06:33Z","timestamp":1757369193000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86486-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030864859","9783030864866"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86486-6_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}