{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:47:20Z","timestamp":1761976040882,"version":"3.41.0"},"reference-count":86,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development","award":["2022YFC2504605"],"award-info":[{"award-number":["2022YFC2504605"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62006207, U20A20387, 62037001"],"award-info":[{"award-number":["62006207, U20A20387, 62037001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2021QNRC001"],"award-info":[{"award-number":["2021QNRC001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ22F020012"],"award-info":[{"award-number":["LZ22F020012"]}]},{"name":"Major Technological Innovation Project of Hangzhou","award":["2022AIZD0147"],"award-info":[{"award-number":["2022AIZD0147"]}]},{"name":"Zhejiang Province Natural Science Foundation","award":["LQ21F020020"],"award-info":[{"award-number":["LQ21F020020"]}]},{"name":"Project by Shanghai AI Laboratory","award":["P22KS00111"],"award-info":[{"award-number":["P22KS00111"]}]},{"name":"Program of Zhejiang Province Science and Technology","award":["2022C01044"],"award-info":[{"award-number":["2022C01044"]}]},{"name":"StarryNight Science Fund of Zhejiang University Shanghai Institute for Advanced Study","award":["SN-ZJU-SIAS-0010"],"award-info":[{"award-number":["SN-ZJU-SIAS-0010"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["226-2022-00142, 226-2022-00051"],"award-info":[{"award-number":["226-2022-00142, 226-2022-00051"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representations with invariant marginal distribution of the input features, however, the invariance of the conditional distribution of the labels given the input features is more essential for unknown domain prediction. Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution. Interestingly, with a causal view on the data generating process, we find that the input features of one domain are valid instrumental variables for other domains. Inspired by this finding, we propose an instrumental variable-driven DG method (IV-DG) by removing the bias of the unobserved confounders with two-stage learning. In the first stage, it learns the conditional distribution of the input features of one domain given input features of another domain. In the second stage, it estimates the relationship by predicting labels with the learned conditional distribution. Theoretical analyses and simulation experiments show that it accurately captures the invariant relationship. Extensive experiments on real-world datasets demonstrate that IV-DG method yields state-of-the-art results.<\/jats:p>","DOI":"10.1145\/3595380","type":"journal-article","created":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T11:13:04Z","timestamp":1682766784000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Instrumental Variable-Driven Domain Generalization with Unobserved Confounders"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0012-7397","authenticated-orcid":false,"given":"Junkun","family":"Yuan","sequence":"first","affiliation":[{"name":"Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2864-4708","authenticated-orcid":false,"given":"Xu","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3701-4428","authenticated-orcid":false,"given":"Ruoxuan","family":"Xiong","sequence":"additional","affiliation":[{"name":"Emory University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7147-5589","authenticated-orcid":false,"given":"Mingming","family":"Gong","sequence":"additional","affiliation":[{"name":"The University of Melbourne"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9690-296X","authenticated-orcid":false,"given":"Xiangyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2139-8807","authenticated-orcid":false,"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Shanghai Institute for Advanced Study of Zhejiang University, ShanghaiAI Laboratory"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4098-588X","authenticated-orcid":false,"given":"Lanfen","family":"Lin","sequence":"additional","affiliation":[{"name":"Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-9790","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Key Laboratory for Corneal Diseases Research of Zhejiang Province"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4j72"},{"key":"e_1_3_1_3_2","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. arXiv:1907.02893. Retrieved from https:\/\/arxiv.org\/abs\/1907.02893."},{"key":"e_1_3_1_4_2","first-page":"998","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Balaji Yogesh","year":"2018","unstructured":"Yogesh Balaji, Swami Sankaranarayanan, and Rama Chellappa. 2018. Metareg: Towards domain generalization using meta-regularization. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 998\u20131008."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454607"},{"key":"e_1_3_1_7_2","first-page":"2178","article-title":"Generalizing from several related classification tasks to a new unlabeled sample","author":"Blanchard Gilles","year":"2011","unstructured":"Gilles Blanchard, Gyemin Lee, and Clayton Scott. 2011. Generalizing from several related classification tasks to a new unlabeled sample. In Proceedings of the 24th International Conference on Neural Information Processing Systems . 2178\u20132186.","journal-title":"Proceedings of the 24th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2935384"},{"key":"e_1_3_1_9_2","first-page":"2224","article-title":"Domain generalization by solving jigsaw puzzles","author":"Carlucci Fabio Maria","year":"2019","unstructured":"Fabio Maria Carlucci, Antonio D\u2019Innocente, S. Bucci, B. Caputo, and T. Tommasi. 2019. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2224\u20132233.","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_18"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2382577.2382582"},{"key":"e_1_3_1_12_2","article-title":"A causal framework for distribution generalization","author":"Christiansen Rune","year":"2021","unstructured":"Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, and Jonas Peters. 2021. A causal framework for distribution generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 10 (2021), 6614\u20136630.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454866"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.11.012"},{"key":"e_1_3_1_15_2","first-page":"1180","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ganin Yaroslav","year":"2015","unstructured":"Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In Proceedings of the International Conference on Machine Learning. PMLR, 1180\u20131189."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"e_1_3_1_17_2","first-page":"2839","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gong Mingming","year":"2016","unstructured":"Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Sch\u00f6lkopf. 2016. Domain adaptation with conditional transferable components. In Proceedings of the International Conference on Machine Learning. PMLR, 2839\u20132848."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188410"},{"key":"e_1_3_1_19_2","first-page":"1414","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Hartford Jason","year":"2017","unstructured":"Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. 2017. Deep IV: A flexible approach for counterfactual prediction. In Proceedings of the International Conference on Machine Learning. 1414\u20131423."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_21_2","volume-title":"Encyclopedia of Actuarial Science","author":"Heyde C.","year":"2006","unstructured":"C. Heyde. 2006. Central limit theorem. Encyclopedia of Actuarial Science."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00922"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.08.016"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_1_27_2","first-page":"1446","article-title":"Episodic training for domain generalization","author":"Li Da","year":"2019","unstructured":"Da Li, J. Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M. Hospedales. 2019. Episodic training for domain generalization. In Proceedings of the IEEE International Conference on Computer Vision . 1446\u20131455.","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495986"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_38"},{"key":"e_1_3_1_32_2","first-page":"3915","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Li Yiying","year":"2019","unstructured":"Yiying Li, Yongxin Yang, Wei Zhou, and Timothy Hospedales. 2019. Feature-critic networks for heterogeneous domain generalization. In Proceedings of the International Conference on Machine Learning. PMLR, 3915\u20133924."},{"key":"e_1_3_1_33_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Liang Jian","year":"2020","unstructured":"Jian Liang, D. Hu, and Jiashi Feng. 2020. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation. In Proceedings of the International Conference on Machine Learning. PMLR."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01336"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01555"},{"key":"e_1_3_1_36_2","first-page":"97","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Long Mingsheng","year":"2015","unstructured":"Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015. Learning transferable features with deep adaptation networks. In Proceedings of the International Conference on Machine Learning. PMLR, 97\u2013105."},{"key":"e_1_3_1_37_2","first-page":"1640","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Long Mingsheng","year":"2018","unstructured":"Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I. Jordan. 2018. Conditional adversarial domain adaptation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 1640\u20131650."},{"key":"e_1_3_1_38_2","first-page":"2208","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Long Mingsheng","year":"2017","unstructured":"Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan. 2017. Deep transfer learning with joint adaptation networks. In Proceedings of the International Conference on Machine Learning. PMLR, 2208\u20132217."},{"key":"e_1_3_1_39_2","unstructured":"Wang Lu Jindong Wang Haoliang Li Yiqiang Chen and Xing Xie. 2022. Domain-invariant feature exploration for domain generalization. arXiv:2207.12020. Retrieved from https:\/\/arxiv.org\/abs\/2207.12020."},{"key":"e_1_3_1_40_2","unstructured":"Zheqi Lv Zhengyu Chen Shengyu Zhang Kun Kuang Wenqiao Zhang Mengze Li Beng Chin Ooi and Fei Wu. 2023. IDEAL: Toward high-efficiency device-cloud collaborative and dynamic recommendation system. arXiv:2302.07335. Retrieved from https:\/\/arxiv.org\/abs\/2302.07335."},{"key":"e_1_3_1_41_2","unstructured":"Zheqi Lv Feng Wang Shengyu Zhang Kun Kuang Hongxia Yang and Fei Wu. 2022. Personalizing intervened network for long-tailed sequential user behavior modeling. arXiv:2208.09130. Retrieved from https:\/\/arxiv.org\/abs\/2208.09130."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583451"},{"key":"e_1_3_1_43_2","article-title":"Adversarial entropy optimization for unsupervised domain adaptation","author":"Ma Ao","year":"2021","unstructured":"Ao Ma, Jingjing Li, Ke Lu, Lei Zhu, and Heng Tao Shen. 2021. Adversarial entropy optimization for unsupervised domain adaptation. IEEE Transactions on Neural Networks and Learning Systems 33, 11 (2021), 6263\u20136274.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.04.086"},{"key":"e_1_3_1_45_2","volume-title":"International Conference on Machine Learning","author":"Mahajan Divyat","year":"2021","unstructured":"Divyat Mahajan, Shruti Tople, and Amit Sharma. 2021. Domain generalization using causal matching. In International Conference on Machine Learning PMLR, 7313\u20137324."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00394"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00737"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6846"},{"key":"e_1_3_1_49_2","unstructured":"Qiaowei Miao Junkun Yuan and Kun Kuang. 2022. Domain generalization via contrastive causal learning. arXiv:2210.02655. Retrieved from https:\/\/arxiv.org\/abs\/2210.02655."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00459"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3263549"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.5555\/1642718"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"e_1_3_1_54_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Piratla Vihari","year":"2020","unstructured":"Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi. 2020. Efficient domain generalization via common-specific low-rank decomposition. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.5555\/1462129"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.012"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12398"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_5"},{"key":"e_1_3_1_61_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Shankar S.","year":"2018","unstructured":"S. Shankar, Vihari Piratla, Soumen Chakrabarti, S. Chaudhuri, P. Jyothi, and Sunita Sarawagi. 2018. Generalizing across domains via cross-gradient training. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454700"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"e_1_3_1_64_2","first-page":"5334","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 5334\u20135344."},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_10"},{"key":"e_1_3_1_66_2","unstructured":"Yufei Wang Haoliang Li Lap-Pui Chau and Alex C. Kot. 2021. Variational disentanglement for domain generalization. arXiv:2109.05826. Retrieved from https:\/\/arxiv.org\/abs\/2109.05826."},{"key":"e_1_3_1_67_2","volume-title":"Tariff on Animal and Vegetable Oils","author":"Wright Philip G.","year":"1928","unstructured":"Philip G. Wright. 1928. Tariff on Animal and Vegetable Oils. Macmillan Company, New York, NY."},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3150807"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3494567"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00151"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3119185"},{"key":"e_1_3_1_72_2","doi-asserted-by":"crossref","unstructured":"Junkun Yuan Xu Ma Defang Chen Fei Wu Lanfen Lin and Kun Kuang. 2023. Collaborative semantic aggregation and calibration for federated domain Generalization. IEEE Transactions on Knowledge and Data Engineering .","DOI":"10.1109\/TKDE.2023.3271851"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548059"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01712-7"},{"issue":"4","key":"e_1_3_1_75_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494568","article-title":"Auto IV: Counterfactual prediction via automatic instrumental variable decomposition","volume":"16","author":"Yuan Junkun","year":"2022","unstructured":"Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, and Lanfen Lin. 2022. Auto IV: Counterfactual prediction via automatic instrumental variable decomposition. ACM Transactions on Knowledge Discovery from Data 16, 4 (2022), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495749"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9542"},{"key":"e_1_3_1_78_2","unstructured":"Kun Zhang Mingming Gong Petar Stojanov Biwei Huang Qingsong Liu and Clark Glymour. 2020. Domain adaptation as a problem of inference on graphical models. In Proceedings of the 34th International Conference on Neural Information Processing Systems ."},{"key":"e_1_3_1_79_2","first-page":"819","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Kun","year":"2013","unstructured":"Kun Zhang, Bernhard Sch\u00f6lkopf, Krikamol Muandet, and Zhikun Wang. 2013. Domain adaptation under target and conditional shift. In Proceedings of the International Conference on Machine Learning. 819\u2013827."},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944455"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00533"},{"key":"e_1_3_1_82_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Yuchen","year":"2019","unstructured":"Yuchen Zhang, Tianle Liu, Mingsheng Long, and Michael I. Jordan. 2019. Bridging theory and algorithm for domain adaptation. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497074"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"e_1_3_1_86_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhou Kaiyang","year":"2021","unstructured":"Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2021. Domain generalization with mixstyle. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.014"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3595380","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3595380","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:48:39Z","timestamp":1750286919000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3595380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,28]]},"references-count":86,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,9,30]]}},"alternative-id":["10.1145\/3595380"],"URL":"https:\/\/doi.org\/10.1145\/3595380","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2023,6,28]]},"assertion":[{"value":"2022-07-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-20","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}