Marcelo Arenas

ExplAIner can express a diverse array of explanation types while ensuring efficient evaluation, transforming how we approach interpretability in machine learning models.

A new compositional language for graph queries reveals that existing standards miss critical expressivity, challenging the status quo of graph query capabilities.