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This paper introduces lifted causal inference, leveraging parametric causal factor graphs to efficiently compute causal effects in relational domains, and presents the Lifted Causal Inference (LCI) algorithm for polynomial-time inference.
This paper presents diffusion models as part of a family of techniques that withhold information and train models to guess it, arguing that diffusion's destroying approach is flexible and advantageous, especially in data-scarce settings; it also discusses exploration problems and introduces a novel kind of probabilistic graphical model.
This paper revisits the theoretical foundations for detecting commutative factors in factor graphs, correcting a previously mistaken sufficient condition and presenting corrected algorithms.