SST trains models to produce concise sufficient reasons as an extra output, yielding faster and often smaller explanations than post-hoc methods like Anchors and SIS.
We first note a known inapproximability result for the Shortest-Implicant-Core prob- lem (Umans (1999)) which will be used to prove the inapproximability result for our case: Lemma
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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons
SST trains models to produce concise sufficient reasons as an extra output, yielding faster and often smaller explanations than post-hoc methods like Anchors and SIS.