arXiv AI
Jul 7

Towards Diverse and Comprehensive Benchmarks for Mutual Information Estimation

arXiv:2607. 03487v1 Announce Type: cross Abstract: Mutual information (MI) estimation is a central problem in machine learning and statistics; however, existing benchmarks typically evaluate estimators on simplified, low-dimensional distributions, leaving their performance on complex, realistic data largely unexplored.

By Alberto Foresti, Ivan Butakov, Alexander Tolmachev, Giulio Franzese, Alexey Frolov, Pietro Michiardi
arXiv Machine Learning
Sep 30

ALICE: In-context, Zero-shot, Mutual Information Estimation

ALICE is a foundation model that estimates mutual information (MI) without per‑distribution training. Trained only on synthetic distributions, it acts as an in‑context estimator of rectified‑flow velocity fields, producing MI via a fixed identity that integrates squared differences between joint and conditional fields. The authors validate ALICE on a challenging benchmark and demonstrate its applicability to unseen data in biology, genetics, and neuroscience, achieving performance comparable to neural estimators trained separately for each distribution.

By Giulio Franzese, Simone Rossi, Pietro Michiardi