The following publications are possibly variants of this publication:
- Learning Invariant Graph Representations for Out-of-Distribution GeneralizationHaoyang Li, Ziwei Zhang, Xin Wang 0019, Wenwu Zhu 0001. nips 2022: [doi]
- Out-of-distribution Generalization with Causal Invariant TransformationsRuoyu Wang 0016, Mingyang Yi, Zhitang Chen, Shengyu Zhu 0001. cvpr 2022: 375-385 [doi]
- Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution GeneralizationShurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji. nips 2023: [doi]
- Enhancing Out-of-distribution Generalization on Graphs via Causal Attention LearningYongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang 0010, Jiancan Wu, Xiangnan He 0001, Tat-Seng Chua. tkdd, 18(5), June 2024. [doi]
- FLOOD: A Flexible Invariant Learning Framework for Out-of-Distribution Generalization on GraphsYang Liu, Xiang Ao 0001, Fuli Feng, Yunshan Ma, Kuan Li, Tat-Seng Chua, Qing He. kdd 2023: 1548-1558 [doi]
- Meta-Causal Feature Learning for Out-of-Distribution GeneralizationYuqing Wang, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li 0003, Xiangxu Meng, Lei Meng. eccv 2023: 530-545 [doi]
- Learning Invariant Representations of Graph Neural Networks via Cluster GeneralizationDonglin Xia, Xiao Wang, Nian Liu, Chuan Shi. nips 2023: [doi]
- Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution GeneralizationJiaxin Qi, Kaihua Tang, Qianru Sun, Xian-Sheng Hua 0001, Hanwang Zhang. eccv 2022: 92-109 [doi]
- On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution GeneralizationShiji Xin, Yifei Wang, Jingtong Su, Yisen Wang 0001. AAAI 2023: 10519-10527 [doi]
- Out-of-Distribution Generalization of Federated Learning via Implicit Invariant RelationshipsYaming Guo, Kai Guo, Xiaofeng Cao 0002, Tieru Wu, Yi Chang. icml 2023: 11905-11933 [doi]