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Found 3 papers, 1 papers with code
Soft Learning Probabilistic Circuits
This paper focuses on the main algorithm for training PCs, LearnSPN, a gold standard due to its efficiency, performance, and ease of use, in particular for tabular data.
Probabilistic Circuits with Constraints via Convex Optimization
This work addresses integrating probabilistic propositional logic constraints into the distribution encoded by a probabilistic circuit (PC).
Cautious Decision-Making for Tree Ensembles
Cautious classifiers are designed to make indeterminate decisions when the uncertainty on the input data or the model output is too high, so as to reduce the risk of making wrong decisions.
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