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There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and AA Lehman · 1968
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Domain adaptation with conditional transferable components
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Dark model adaptation: Semantic image segmentation from daytime to nighttime
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The vapnik-chervonenkis dimension of graph and recursive neural networks
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Pitfalls of graph neural network evaluation
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Systematic generalisation with group invariant predictions
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Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
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Size-invariant graph representations for graph classification extrapolations
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A unified lottery ticket hypothesis for graph neural networks
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One pixel attack for fooling deep neural networks
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Stability and generalization of graph convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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Bayesian graph convolutional neural networks for semi-supervised classification
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Invariant risk minimization games
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Invariant rationalization
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Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2021
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Environment inference for invariant learning
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Ai for radiographic covid-19 detection selects shortcuts over signal
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An information-theoretic approach to distribution shifts
Marco Federici, Ryota Tomioka, and Patrick Forré · 2021
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When is invariance useful in an out-of-distribution generalization problem ?
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Out-of-distribution generalization via risk extrapolation (rex)
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim · 2021
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Heterogeneous risk minimization
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Subgroup generalization and fairness of graph neural networks
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Domain generalization using causal matching
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Multi-scale attributed node embedding
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How neural networks extrapolate: From feedforward to graph neural networks
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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
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2021
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