ALICE: In-context, Zero-shot, Mutual Information Estimation
Estimating mutual information (MI) from samples is a central objective in a variety of scientific fields. Modern neural estimators are accurate in the large-data regime, but they fall short when data is scarce, and each must be fit anew for every distribution under study. Current estimators are moreover tied to specific data types. These constraints limit their adoption in many applications where per-distribution training is impractical and sample sizes are small. We present ALICE, a foundation model that removes per-distribution training, while achieving competitive estimation accuracy. Trained exclusively on a broad family of synthetic distributions, ALICE acts as an in-context estimato...
| Authors | Giulio Franzese, Simone Rossi, Pietro Michiardi |
|---|---|
| Primary category | cs.LG |
| Published | 28 September 2026 |
| Last updated at source | 29 September 2026 |
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