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cs.LG2025
Sum of Squares Circuits
Lorenzo Loconte, Stefan Mengel, Antonio Vergari
Designing expressive generative models that support exact and efficient inference is a core question in probabilistic ML. Probabilistic circuits (PCs) offer a framework where this…
cs.LG2024
Learning Model Agnostic Explanations via Constraint Programming
Frederic Koriche, Jean-Marie Lagniez, Stefan Mengel +1
Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in t…
cs.LG2024
Subtractive Mixture Models via Squaring: Representation and Learning
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel +4
Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically re…