AI RESEARCH
CASHomon Sets: Efficient Rashomon Sets Across Multiple Model Classes and their Hyperparameters
arXiv CS.LG
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ArXi:2603.15321v1 Announce Type: new Rashomon sets are model sets within one model class that perform nearly as well as a reference model from the same model class. They reveal the existence of alternative well-performing models, which may different interpretations. This enables selecting models that match domain knowledge, hidden constraints, or user preferences. However, efficient construction methods currently exist for only a few model classes. Applied machine learning usually searches many model classes, and the best class is unknown beforehand.