​A two session lecture on July 18 2025 (10:00–10:50 and 11:10-12:00) at P3.10@Técnico and Online with

Mário Figueiredo, IT & Instituto Superior Técnico
Fenchel-Young Variational Learning

This lecture first provides an introduction to classical variational inference (VI), a key technique for approximating complex posterior distributions in Bayesian methods, typically by minimizing the Kullback-Leibler (KL) divergence. We'll discuss its principles and common uses.

Building on this, the lecture introduces Fenchel-Young variational inference (FYVI), a novel generalization that enhances flexibility. FYVI replaces the KL divergence with broader Fenchel-Young (FY) regularizers, with a special focus on those derived from Tsallis entropies. This approach enables learning posterior distributions with significantly smaller, or sparser, support than the prior, offering advantages in model interpretability and performance.

Permanent link to this information: https://m4ai.math.tecnico.ulisboa.pt/lecture_series?sgid=104