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This paper proposes Neural-Bayesian Structure Learning, a framework that integrates differentiable structure learning with discrete choice modeling to predict choice behavior and evaluate interventions, achieving comparable performance while recovering coherent dependency structures.
This paper proposes a supervised structure learning method for training agent-curated knowledge bases, treating the store as a model to improve retrieval accuracy and reduce action usage in retrieval-augmented generation systems.
This paper theoretically analyzes support selection in continuous DAG learning, showing that smooth acyclicity constraints alone cannot rank supports beyond feasibility and deriving selection times for NOTEARS/DAGMA, with empirical audits on 320 trajectories.
This paper presents a polynomial-time algorithm for learning the structure of a Gaussian graphical model from a single trajectory of Glauber dynamics, with a trajectory-length guarantee that does not depend on the mixing time.