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Author: judiebig

README.md

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-**TL;DR: Our ICML paper proposed Explanation Ensemble (EE), which improves the trustworthiness of SI-GNNs by aggregating multiple explanations from independently trained models. While effective, it has high computational cost during inference (limits its deployment) and is incompatible with single-explanation metrics such as FID (limits its evaluation). In this extension, we propose Consensus Distillation (CD), which distills the ensemble’s consensus knowledge into a single model, retaining EE’s capability while addressing its limitations.
+TL;DR: Our ICML paper proposed Explanation Ensemble (EE), which improves the trustworthiness of SI-GNNs by aggregating multiple explanations from independently trained models. While effective, it has high computational cost during inference (limits its deployment) and is incompatible with single-explanation metrics such as FID (limits its evaluation). In this extension, we propose Consensus Distillation (CD), which distills the ensemble’s consensus knowledge into a single model, retaining EE’s capability while addressing its limitations.
 
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