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We tackle the problem of graph out-of-distribution (OOD) generalization.
What can neural networks reason about?
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2012
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Information network or social network? the structure of the twitter follow graph
Seth A Myers, Aneesh Sharma, Pankaj Gupta, and Jimmy Lin · 2014
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Domain-adversarial neural networks
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Visual domain adaptation: A survey of recent advances
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Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang · 2016
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Learning to adapt structured output space for semantic segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, and Manmohan Chandraker · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Measuring abstract reasoning in neural networks
David Barrett, Felix Hill, Adam Santoro, Ari Morcos, and Timothy Lillicrap · 2018
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MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Out-of-distribution generalization via risk extrapolation (REx)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Invariant causal representation learning for out-of-distribution generalization
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, and Bernhard Schölkopf · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Invariance principle meets information bottleneck for out-of-distribution generalization
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish · 2021
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Contrastive adaptation network for unsupervised domain adaptation
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann · 2019
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A generalization error bound for multi-class domain generalization
Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Urun Dogan, James W Cutler, and Clayton Scott · 2019
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Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
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Neural execution of graph algorithms
Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, and Charles Blundell · 2019
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Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
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Learning models with uniform performance via distributionally robust optimization
John C Duchi and Hongseok Namkoong · 2021
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Transformer-based source-free domain adaptation
Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, and Elisa Ricci · 2021
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Masato Ishii and Masashi Sugiyama · 2021
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Adapting off-the-shelf source segmenter for target medical image segmentation
Xiaofeng Liu, Fangxu Xing, Chao Yang, Georges El Fakhri, and Jonghye Woo · 2021
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Source-free adaptation to measurement shift via bottom-up feature restoration
Cian Eastwood, Ian Mason, Christopher KI Williams, and Bernhard Schölkopf · 2021
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From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron · 2021
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Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
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Mixup for node and graph classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi · 2021
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On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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Topology-aware graph pooling networks
Hongyang Gao, Yi Liu, and Shuiwang Ji · 2021
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Interpretable and generalizable graph learning via stochastic attention mechanism
Siqi Miao, Mia Liu, and Pan Li · 2022
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Learning invariant graph representations for out-of-distribution generalization
Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2022
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Zin: When and how to learn invariance without environment partition?
Yong Lin, Shengyu Zhu, Lu Tan, and Peng Cui · 2022
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip Yu · 2022
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Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, et al · 2022
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Modeling the data-generating process is necessary for out-of-distribution generalization
Jivat Neet Kaur, Emre Kiciman, and Amit Sharma · 2022
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Source-free unsupervised domain adaptation: A survey
Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu · 2022
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Self-supervised noisy label learning for source-free unsupervised domain adaptation
Weijie Chen, Luojun Lin, Shicai Yang, Di Xie, Shiliang Pu, and Yueting Zhuang · 2022
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A source-free domain adaptive polyp detection framework with style diversification flow
Xinyu Liu and Yixuan Yuan · 2022
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Source-free domain adaptation for real-world image dehazing
Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao · 2022
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Lattice convolutional networks for learning ground states of quantum many-body systems, 2022
Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, and Shuiwang Ji · 2022
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang · 2022
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Generalization analysis of message passing neural networks on large random graphs
Sohir Maskey, Ron Levie, Yunseok Lee, and Gitta Kutyniok · 2022
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Your neighbors are communicating: Towards powerful and scalable graph neural networks
Meng Liu, Haiyang Yu, and Shuiwang Ji · 2022
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Graph structure and feature extrapolation for out-of-distribution generalization, 2023
Xiner Li, Shurui Gui, Youzhi Luo, and Shuiwang Ji · 2023
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