1 citations · 1 across the 6 of their papers we have counts for
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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…
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…
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…