RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
Good: A graph out-of-distribution benchmark.arXiv preprint arXiv:2206.08452
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
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Adding Bertz index regression and MMFF94 strain penalty as auxiliary losses to a GINE GNN yields small but statistically significant OOD AUC improvements (up to +0.0066) on natural products when trained on drug-like molecules.
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Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
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Physics-Aware Auxiliary Losses Improve Out-of-Distribution Generalization of a GNN Synthesizability Filter
Adding Bertz index regression and MMFF94 strain penalty as auxiliary losses to a GINE GNN yields small but statistically significant OOD AUC improvements (up to +0.0066) on natural products when trained on drug-like molecules.