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Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.
What can neural networks reason about?
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Dataset shift in machine learning
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Causality
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Auto-encoding variational bayes
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Domain generalization via invariant feature representation
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Causal inference by using invariant prediction: identification and confidence intervals
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Neural message passing for quantum chemistry
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Semi-supervised classification with graph convolutional networks
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mixup: Beyond empirical risk minimization
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Graph Attention Networks
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A generalization error bound for multi-class domain generalization
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Understanding attention and generalization in graph neural networks
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2019
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Large-scale representation learning on graphs via bootstrapping
Thakoor, S., Tallec, C., Azar, M. G., Azabou, M., Dyer, E. L., Munos, R., Veličković, P., and Valko, M · 2021
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From local structures to size generalization in graph neural networks
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Shift-robust GNNs: Overcoming the limitations of localized graph training data
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A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
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Neural execution of graph algorithms
Veličković, P., Ying, R., Padovano, M., Hadsell, R., and Blundell, C · 2019
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Featureflow: Robust video interpolation via structure-to-texture generation
Gui, S., Wang, C., Chen, Q., and Tao, D · 2020
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In search of lost domain generalization
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Denoising diffusion probabilistic models
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Open graph benchmark: Datasets for machine learning on graphs
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3dlinker: An e (3) equivariant variational autoencoder for molecular linker design
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Equivariant 3d-conditional diffusion models for molecular linker design
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Robust optimization as data augmentation for large-scale graphs
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Extramix: Extrapolatable data augmentation for regression using generative models
Kwon, K., Jeong, K., Park, S., Park, S., Lee, H., Kwak, S.-Y., Kim, S., and Cho, K · 2022
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Learning invariant graph representations for out-of-distribution generalization
Li, H., Zhang, Z., Wang, X., and Zhu, W · 2022
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Zin: When and how to learn invariance without environment partition?
Lin, Y., Zhu, S., Tan, L., and Cui, P · 2022
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Graph rationalization with environment-based augmentations
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Interpretable and generalizable graph learning via stochastic attention mechanism
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Graph transplant: Node saliency-guided graph mixup with local structure preservation
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Adversarial causal augmentation for graph covariate shift
Sui, Y., Wang, X., Wu, J., Zhang, A., and He, X · 2022
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Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., and Yu, P · 2022
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Improving out-of-distribution robustness via selective augmentation
Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., and Finn, C · 2022
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Finding diverse and predictable subgraphs for graph domain generalization
Yu, J., Liang, J., and He, R · 2022
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NICO++: Towards better benchmarking for domain generalization
Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., and Liu, H · 2022
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Gui, S., Liu, M., Li, X., Luo, Y., and Ji, S · 2023
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