A new PAC-Bayesian framework for GCNs derives a family of generalization bounds that embed graph topology via structured sensitivity matrices from spatial and spectral perspectives, recovering prior bounds as special cases while claiming tighter results.
PAC-Bayesian adversarially robust generalization bounds for graph neural network
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Extends sensitivity-aware PAC-Bayes to MPGNNs for tighter adversarial robust generalization bounds by using Jacobian-aligned anisotropic Gaussians that reduce hidden-width dependence to the number of classes K.
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Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks
A new PAC-Bayesian framework for GCNs derives a family of generalization bounds that embed graph topology via structured sensitivity matrices from spatial and spectral perspectives, recovering prior bounds as special cases while claiming tighter results.
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PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Extends sensitivity-aware PAC-Bayes to MPGNNs for tighter adversarial robust generalization bounds by using Jacobian-aligned anisotropic Gaussians that reduce hidden-width dependence to the number of classes K.