This work proposes two complementary families of tests: a copula-first family that systematically varies ground-truth mutual information, dimensionality, and marginal complexity using synthetic and flow-based transformations; and a marginals-first family that couples real-world image data with controlled dependency str...
Alberto Foresti, I. Butakov, Alexander Tolmachev et al.· arXiv.org· 0 citations
Information-theoretic measures, such as Mutual Information (MI), play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific disciplines. Yet, their use on real-world discrete data remains challenging. Existing methods typically rely on embedding discrete...
Alberto Foresti, Giulio Franzese, Pietro Michiardi· 0 citations
Across several benchmarks, this method outperforms the strongest published method in settings involving combinatorial and unseen perturbation prediction, and is the strongest published method in settings involving combinatorial and unseen perturbation prediction.
Mustapha Bounoua, G. Franzese, P. Michiardi· 0 citations
For the first time, it is shown that a single model closes the gap with neural estimators trained separately for each distribution, while natively supporting different data dimensionality and sample cardinality, enabling zero-shot MI analysis across scientific domains.
G. Franzese, Simone Rossi, P. Michiardi· 0 citations
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