ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
Advances in neural information processing systems , volume=
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The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time method that adapts pretrained tabular foundation models to strategic feature manipulation by constructing aligned in-context examples.
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ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
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When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time method that adapts pretrained tabular foundation models to strategic feature manipulation by constructing aligned in-context examples.