{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:45:31Z","timestamp":1777657531586,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":74,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shanghai AI Laboratory","award":["P22KS00111"],"award-info":[{"award-number":["P22KS00111"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006207, 62037001, U20A20387"],"award-info":[{"award-number":["62006207, 62037001, U20A20387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province Natural Science Foundation","award":["LQ21F020020"],"award-info":[{"award-number":["LQ21F020020"]}]},{"name":"Zhejiang Province Science and Technology","award":["2022C01044"],"award-info":[{"award-number":["2022C01044"]}]},{"name":"the StarryNight Science Fund of Zhejiang University Shanghai Institute for Advanced Study","award":["SN-ZJU-SIAS-0010"],"award-info":[{"award-number":["SN-ZJU-SIAS-0010"]}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2021QNRC001"],"award-info":[{"award-number":["2021QNRC001"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["226-2022-00142, 226-2022-00051"],"award-info":[{"award-number":["226-2022-00142, 226-2022-00051"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599481","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"2189-2200","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0305-0059","authenticated-orcid":false,"given":"Yunze","family":"Tong","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0012-7397","authenticated-orcid":false,"given":"Junkun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1289-1571","authenticated-orcid":false,"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6892-5357","authenticated-orcid":false,"given":"Didi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7883-0552","authenticated-orcid":false,"given":"Keli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2139-8807","authenticated-orcid":false,"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-9790","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Invariant risk minimization. arXiv preprint arXiv:1907.02893","author":"Arjovsky Martin","year":"2019","unstructured":"Martin Arjovsky , L\u00e9on Bottou , Ishaan Gulrajani , and David Lopez-Paz . 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 ( 2019 ). Martin Arjovsky, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16829"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"e_1_3_2_2_4_1","first-page":"1","article-title":"Domain Generalization by Marginal Transfer Learning","volume":"22","author":"Blanchard Gilles","year":"2021","unstructured":"Gilles Blanchard , Aniket Anand Deshmukh , Urun Dogan , Gyemin Lee , and Clayton Scott . 2021 . Domain Generalization by Marginal Transfer Learning . Journal of Machine Learning Research , Vol. 22 , 2 (2021), 1 -- 55 . Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, and Clayton Scott. 2021. Domain Generalization by Marginal Transfer Learning. Journal of Machine Learning Research, Vol. 22, 2 (2021), 1--55.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_5_1","volume-title":"SWAD: Domain Generalization by Seeking Flat Minima. In Advances in Neural Information Processing Systems (NeurIPS).","author":"Cha Junbum","year":"2021","unstructured":"Junbum Cha , Sanghyuk Chun , Kyungjae Lee , Han-Cheol Cho , Seunghyun Park , Yunsung Lee , and Sungrae Park . 2021 . SWAD: Domain Generalization by Seeking Flat Minima. In Advances in Neural Information Processing Systems (NeurIPS). Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, and Sungrae Park. 2021. SWAD: Domain Generalization by Seeking Flat Minima. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_2_6_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 . A simple framework for contrastive learning of visual representations . In International conference on machine learning. PMLR, 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_2_7_1","volume-title":"Invariant causal mechanisms through distribution matching. arXiv preprint arXiv:2206.11646","author":"Chevalley Mathieu","year":"2022","unstructured":"Mathieu Chevalley , Charlotte Bunne , Andreas Krause , and Stefan Bauer . 2022. Invariant causal mechanisms through distribution matching. arXiv preprint arXiv:2206.11646 ( 2022 ). Mathieu Chevalley, Charlotte Bunne, Andreas Krause, and Stefan Bauer. 2022. Invariant causal mechanisms through distribution matching. arXiv preprint arXiv:2206.11646 (2022)."},{"key":"e_1_3_2_2_8_1","volume-title":"Environment Inference for Invariant Learning. In International Conference on Machine Learning.","author":"Creager Elliot","year":"2021","unstructured":"Elliot Creager , J\u00f6rn-Henrik Jacobsen , and Richard Zemel . 2021 . Environment Inference for Invariant Learning. In International Conference on Machine Learning. Elliot Creager, J\u00f6rn-Henrik Jacobsen, and Richard Zemel. 2021. Environment Inference for Invariant Learning. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_9_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Machine Learning. PMLR, 2922--2932","author":"Engstrom Logan","year":"2020","unstructured":"Logan Engstrom , Andrew Ilyas , Shibani Santurkar , Dimitris Tsipras , Jacob Steinhardt , and Aleksander Madry . 2020 . Identifying statistical bias in dataset replication . In International Conference on Machine Learning. PMLR, 2922--2932 . Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Jacob Steinhardt, and Aleksander Madry. 2020. Identifying statistical bias in dataset replication. In International Conference on Machine Learning. PMLR, 2922--2932."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.208"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_3_2_2_14_1","volume-title":"In Search of Lost Domain Generalization. In International Conference on Learning Representations.","author":"Gulrajani Ishaan","year":"2021","unstructured":"Ishaan Gulrajani and David Lopez-Paz . 2021 . In Search of Lost Domain Generalization. In International Conference on Learning Representations. Ishaan Gulrajani and David Lopez-Paz. 2021. In Search of Lost Domain Generalization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/3477.764879"},{"key":"e_1_3_2_2_19_1","volume-title":"Remi Le Priol, and Aaron Courville","author":"Krueger David","year":"2020","unstructured":"David Krueger , Ethan Caballero , Joern-Henrik Jacobsen , Amy Zhang , Jonathan Binas , Remi Le Priol, and Aaron Courville . 2020 . Out-of-distribution generalization via risk extrapolation (rex). arXiv preprint arXiv:2003.00688 (2020). David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville. 2020. Out-of-distribution generalization via risk extrapolation (rex). arXiv preprint arXiv:2003.00688 (2020)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.","author":"Li Mengze","year":"2023","unstructured":"Mengze Li , Han Wang , Wenqiao Zhang , Jiaxu Miao , Wei Ji , Zhou Zhao , Shengyu Zhang , and Fei Wu . 2023 b. WINNER: Weakly-supervised hIerarchical decompositioN and aligNment for spatio-tEmporal video gRounding . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Mengze Li, Han Wang, Wenqiao Zhang, Jiaxu Miao, Wei Ji, Zhou Zhao, Shengyu Zhang, and Fei Wu. 2023 b. WINNER: Weakly-supervised hIerarchical decompositioN and aligNment for spatio-tEmporal video gRounding. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).","author":"Li Mengze","year":"2023","unstructured":"Mengze Li , Tianbao Wang , Jiahe Xu , Kairong Han , Shengyu Zhang , Zhou Zhao , Jiaxu Miao , Wenqiao Zhang , Shiliang Pu , and Fei Wu . 2023 a. Multi-modal Action Chain Abductive Reasoning . In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, and Fei Wu. 2023 a. Multi-modal Action Chain Abductive Reasoning. In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548333"},{"key":"e_1_3_2_2_26_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li , Anit Kumar Sahu , Manzil Zaheer , Maziar Sanjabi , Ameet Talwalkar , and Virginia Smith . 2020 . Federated optimization in heterogeneous networks . Proceedings of Machine Learning and Systems , Vol. 2 (2020), 429 -- 450 . Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, Vol. 2 (2020), 429--450.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11682"},{"key":"e_1_3_2_2_28_1","volume-title":"Decorr: Environment Partitioning for Invariant Learning and OOD Generalization. arXiv preprint arXiv:2211.10054","author":"Liao Yufan","year":"2022","unstructured":"Yufan Liao , Qi Wu , and Xing Yan . 2022 . Decorr: Environment Partitioning for Invariant Learning and OOD Generalization. arXiv preprint arXiv:2211.10054 (2022). Yufan Liao, Qi Wu, and Xing Yan. 2022. Decorr: Environment Partitioning for Invariant Learning and OOD Generalization. arXiv preprint arXiv:2211.10054 (2022)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01555"},{"key":"e_1_3_2_2_30_1","first-page":"24529","article-title":"ZIN: When and How to Learn Invariance Without Environment Partition","volume":"35","author":"Lin Yong","year":"2022","unstructured":"Yong Lin , Shengyu Zhu , Lu Tan , and Peng Cui . 2022 b. ZIN: When and How to Learn Invariance Without Environment Partition ? Advances in Neural Information Processing Systems , Vol. 35 (2022), 24529 -- 24542 . Yong Lin, Shengyu Zhu, Lu Tan, and Peng Cui. 2022b. ZIN: When and How to Learn Invariance Without Environment Partition? Advances in Neural Information Processing Systems, Vol. 35 (2022), 24529--24542.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_31_1","volume-title":"International Conference on Machine Learning. PMLR, 6804--6814","author":"Liu Jiashuo","year":"2021","unstructured":"Jiashuo Liu , Zheyuan Hu , Peng Cui , Bo Li , and Zheyan Shen . 2021 a. Heterogeneous risk minimization . In International Conference on Machine Learning. PMLR, 6804--6814 . Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen. 2021a. Heterogeneous risk minimization. In International Conference on Machine Learning. PMLR, 6804--6814."},{"key":"e_1_3_2_2_32_1","volume-title":"Kernelized heterogeneous risk minimization. arXiv preprint arXiv:2110.12425","author":"Liu Jiashuo","year":"2021","unstructured":"Jiashuo Liu , Zheyuan Hu , Peng Cui , Bo Li , and Zheyan Shen . 2021b. Kernelized heterogeneous risk minimization. arXiv preprint arXiv:2110.12425 ( 2021 ). Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen. 2021b. Kernelized heterogeneous risk minimization. arXiv preprint arXiv:2110.12425 (2021)."},{"key":"e_1_3_2_2_33_1","volume-title":"Measure the Predictive Heterogeneity. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=g2oB_k-18b","author":"Liu Jiashuo","year":"2023","unstructured":"Jiashuo Liu , Jiayun Wu , Renjie Pi , Renzhe Xu , Xingxuan Zhang , Bo Li , and Peng Cui . 2023 . Measure the Predictive Heterogeneity. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=g2oB_k-18b Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, and Peng Cui. 2023. Measure the Predictive Heterogeneity. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=g2oB_k-18b"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00788"},{"key":"e_1_3_2_2_35_1","volume-title":"Beng Chin Ooi, and Fei Wu. 2023 a. IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System. arXiv preprint arXiv:2302.07335","author":"Lv Zheqi","year":"2023","unstructured":"Zheqi Lv , Zhengyu Chen , Shengyu Zhang , Kun Kuang , Wenqiao Zhang , Mengze Li , Beng Chin Ooi, and Fei Wu. 2023 a. IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System. arXiv preprint arXiv:2302.07335 ( 2023 ). Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, and Fei Wu. 2023 a. IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System. arXiv preprint arXiv:2302.07335 (2023)."},{"key":"e_1_3_2_2_36_1","volume-title":"Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling. arXiv preprint arXiv:2208.09130","author":"Lv Zheqi","year":"2022","unstructured":"Zheqi Lv , Feng Wang , Shengyu Zhang , Kun Kuang , Hongxia Yang , and Fei Wu. 2022b. Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling. arXiv preprint arXiv:2208.09130 ( 2022 ). Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang, Hongxia Yang, and Fei Wu. 2022b. Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling. arXiv preprint arXiv:2208.09130 (2022)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583451"},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning. PMLR, 7313--7324","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--7324 . Divyat Mahajan, Shruti Tople, and Amit Sharma. 2021. Domain generalization using causal matching. In International Conference on Machine Learning. PMLR, 7313--7324."},{"key":"e_1_3_2_2_39_1","volume-title":"Domain Generalization via Contrastive Causal Learning. arXiv preprint arXiv:2210.02655","author":"Miao Qiaowei","year":"2022","unstructured":"Qiaowei Miao , Junkun Yuan , and Kun Kuang . 2022. Domain Generalization via Contrastive Causal Learning. arXiv preprint arXiv:2210.02655 ( 2022 ). Qiaowei Miao, Junkun Yuan, and Kun Kuang. 2022. Domain Generalization via Contrastive Causal Learning. arXiv preprint arXiv:2210.02655 (2022)."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Learning Representations.","author":"Montero Milton Llera","year":"2020","unstructured":"Milton Llera Montero , Casimir JH Ludwig , Rui Ponte Costa , Gaurav Malhotra , and Jeffrey Bowers . 2020 . The role of disentanglement in generalisation . In International Conference on Learning Representations. Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers. 2020. The role of disentanglement in generalisation. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3263549"},{"key":"e_1_3_2_2_43_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord , Yazhe Li , and Oriol Vinyals . 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 ( 2018 ). Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Machine Learning. PMLR, 7728--7738","author":"Piratla Vihari","year":"2020","unstructured":"Vihari Piratla , Praneeth Netrapalli , and Sunita Sarawagi . 2020 . Efficient domain generalization via common-specific low-rank decomposition . In International Conference on Machine Learning. PMLR, 7728--7738 . Vihari Piratla, Praneeth Netrapalli, and Sunita Sarawagi. 2020. Efficient domain generalization via common-specific low-rank decomposition. In International Conference on Machine Learning. PMLR, 7728--7738."},{"key":"e_1_3_2_2_45_1","volume-title":"International Conference on Machine Learning. PMLR, 5389--5400","author":"Recht Benjamin","year":"2019","unstructured":"Benjamin Recht , Rebecca Roelofs , Ludwig Schmidt , and Vaishaal Shankar . 2019 . Do imagenet classifiers generalize to imagenet? . In International Conference on Machine Learning. PMLR, 5389--5400 . Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. Do imagenet classifiers generalize to imagenet?. In International Conference on Machine Learning. PMLR, 5389--5400."},{"key":"e_1_3_2_2_46_1","volume-title":"Optimal representations for covariate shift. arXiv preprint arXiv:2201.00057","author":"Ruan Yangjun","year":"2021","unstructured":"Yangjun Ruan , Yann Dubois , and Chris J Maddison . 2021. Optimal representations for covariate shift. arXiv preprint arXiv:2201.00057 ( 2021 ). Yangjun Ruan, Yann Dubois, and Chris J Maddison. 2021. Optimal representations for covariate shift. arXiv preprint arXiv:2201.00057 (2021)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein etal 2015. Imagenet large scale visual recognition challenge. International journal of computer vision Vol. 115 3 (2015) 211--252.  Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision Vol. 115 3 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_2_48_1","volume-title":"Distributionally Robust Neural Networks. In International Conference on Learning Representations.","author":"Shiori","year":"2020","unstructured":"Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto , and Percy Liang . 2020 . Distributionally Robust Neural Networks. In International Conference on Learning Representations. Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang. 2020. Distributionally Robust Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_49_1","volume-title":"Gradient Matching for Domain Generalization. In International Conference on Learning Representations.","author":"Shi Yuge","year":"2022","unstructured":"Yuge Shi , Jeffrey Seely , Philip Torr , Siddharth N, Awni Hannun , Nicolas Usunier , and Gabriel Synnaeve . 2022 . Gradient Matching for Domain Generalization. In International Conference on Learning Representations. Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. 2022. Gradient Matching for Domain Generalization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539444"},{"key":"e_1_3_2_2_52_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of machine learning research , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"e_1_3_2_2_55_1","volume-title":"Generalizing to unseen domains via adversarial data augmentation. Advances in 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. Advances in neural information processing systems , Vol. 31 ( 2018 ). Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_56_1","first-page":"18225","article-title":"Self-supervised learning disentangled group representation as feature","volume":"34","author":"Wang Tan","year":"2021","unstructured":"Tan Wang , Zhongqi Yue , Jianqiang Huang , Qianru Sun , and Hanwang Zhang . 2021 . Self-supervised learning disentangled group representation as feature . Advances in Neural Information Processing Systems , Vol. 34 (2021), 18225 -- 18240 . Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, and Hanwang Zhang. 2021. Self-supervised learning disentangled group representation as feature. Advances in Neural Information Processing Systems, Vol. 34 (2021), 18225--18240.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3150807"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467214"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"e_1_3_2_2_60_1","volume-title":"Improve unsupervised domain adaptation with mixup training. arXiv preprint arXiv:2001.00677","author":"Yan Shen","year":"2020","unstructured":"Shen Yan , Huan Song , Nanxiang Li , Lincan Zou , and Liu Ren . 2020. Improve unsupervised domain adaptation with mixup training. arXiv preprint arXiv:2001.00677 ( 2020 ). Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, and Liu Ren. 2020. Improve unsupervised domain adaptation with mixup training. arXiv preprint arXiv:2001.00677 (2020)."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548059"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01712-7"},{"key":"e_1_3_2_2_63_1","volume-title":"2023 b. Instrumental Variable-Driven Domain Generalization with Unobserved Confounders. ACM Transactions on Knowledge Discovery from Data","author":"Yuan Junkun","year":"2023","unstructured":"Junkun Yuan , Xu Ma , Ruoxuan Xiong , Mingming Gong , Xiangyu Liu , Fei Wu , Lanfen Lin , and Kun Kuang . 2023 b. Instrumental Variable-Driven Domain Generalization with Unobserved Confounders. ACM Transactions on Knowledge Discovery from Data ( 2023 ). Junkun Yuan, Xu Ma, Ruoxuan Xiong, Mingming Gong, Xiangyu Liu, Fei Wu, Lanfen Lin, and Kun Kuang. 2023 b. Instrumental Variable-Driven Domain Generalization with Unobserved Confounders. ACM Transactions on Knowledge Discovery from Data (2023)."},{"key":"e_1_3_2_2_64_1","volume-title":"Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=woa783QMul","author":"Zhang Fengda","year":"2023","unstructured":"Fengda Zhang , Kun Kuang , Long Chen , Yuxuan Liu , Chao Wu , and Jun Xiao . 2023 . Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=woa783QMul Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, and Jun Xiao. 2023. Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=woa783QMul"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00786"},{"key":"e_1_3_2_2_66_1","volume-title":"Tel Aviv","author":"Zhang Min","year":"2022","unstructured":"Min Zhang , Siteng Huang , Wenbin Li , and Donglin Wang . 2022 a. Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. In Computer Vision-ECCV 2022: 17th European Conference , Tel Aviv , Israel, October 23-27, 2022, Proceedings, Part XX. Springer, 453--470. Min Zhang, Siteng Huang, Wenbin Li, and Donglin Wang. 2022a. Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XX. Springer, 453--470."},{"key":"e_1_3_2_2_67_1","volume-title":"Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. arXiv preprint arXiv:2007.02931","author":"Zhang Marvin","year":"2020","unstructured":"Marvin Zhang , Henrik Marklund , Abhishek Gupta , Sergey Levine , and Chelsea Finn . 2020. Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. arXiv preprint arXiv:2007.02931 ( 2020 ). Marvin Zhang, Henrik Marklund, Abhishek Gupta, Sergey Levine, and Chelsea Finn. 2020. Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. arXiv preprint arXiv:2007.02931 (2020)."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462908"},{"key":"e_1_3_2_2_69_1","volume-title":"Domain generalization with optimal transport and metric learning. arXiv preprint arXiv:2007.10573","author":"Zhou Fan","year":"2020","unstructured":"Fan Zhou , Zhuqing Jiang , Changjian Shui , Boyu Wang , and Brahim Chaib-draa. 2020. Domain generalization with optimal transport and metric learning. arXiv preprint arXiv:2007.10573 ( 2020 ). Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang, and Brahim Chaib-draa. 2020. Domain generalization with optimal transport and metric learning. arXiv preprint arXiv:2007.10573 (2020)."},{"key":"e_1_3_2_2_70_1","volume-title":"Domain Generalization with MixStyle. In 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 International Conference on Learning Representations. Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. 2021. Domain Generalization with MixStyle. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_71_1","volume-title":"International Conference on Machine Learning. PMLR, 27203--27221","author":"Zhou Xiao","year":"2022","unstructured":"Xiao Zhou , Yong Lin , Renjie Pi , Weizhong Zhang , Renzhe Xu , Peng Cui , and Tong Zhang . 2022 a. Model agnostic sample reweighting for out-of-distribution learning . In International Conference on Machine Learning. PMLR, 27203--27221 . Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, and Tong Zhang. 2022a. Model agnostic sample reweighting for out-of-distribution learning. In International Conference on Machine Learning. PMLR, 27203--27221."},{"key":"e_1_3_2_2_72_1","volume-title":"International Conference on Machine Learning. PMLR, 27222--27244","author":"Zhou Xiao","year":"2022","unstructured":"Xiao Zhou , Yong Lin , Weizhong Zhang , and Tong Zhang . 2022 b. Sparse invariant risk minimization . In International Conference on Machine Learning. PMLR, 27222--27244 . Xiao Zhou, Yong Lin, Weizhong Zhang, and Tong Zhang. 2022b. Sparse invariant risk minimization. In International Conference on Machine Learning. PMLR, 27222--27244."},{"key":"e_1_3_2_2_73_1","volume-title":"Universal Domain Adaptation via Compressive Attention Matching. arXiv preprint arXiv:2304.11862","author":"Zhu Didi","year":"2023","unstructured":"Didi Zhu , Yincuan Li , Junkun Yuan , Zexi Li , Yunfeng Shao , Kun Kuang , and Chao Wu. 2023. Universal Domain Adaptation via Compressive Attention Matching. arXiv preprint arXiv:2304.11862 ( 2023 ). Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, Yunfeng Shao, Kun Kuang, and Chao Wu. 2023. Universal Domain Adaptation via Compressive Attention Matching. arXiv preprint arXiv:2304.11862 (2023)."},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00886-5"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599481","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599481","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:37Z","timestamp":1750178257000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":74,"alternative-id":["10.1145\/3580305.3599481","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599481","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}