Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Shruti
Joshi, le 16 septembre à 15h00 (mode hybride).

Titre : Real-world Implications of Identifiable Representation Learning

Date: mardi 16 septembre à 15h

Location: Agora, MILA, 6650 rue Saint-Urbain, 1er étage

Jury

Résumé

A core objective in unsupervised learning is to uncover latent structure that explains observed data. Identifiable representation learning addresses this challenge by requiring that latent representations be uniquely determined up to irrelevant indeterminacies. Despite progress since the 1930s, it remains underutilised in real-world domains. Interpretability is a natural proving ground, as both aim to recover latent variables that correspond to semantic factors or concepts. In this view, interpretability is not merely about producing explanations palatable to human readers; it is about establishing that a model’s internal representations are uniquely tied, up to benign symmetries, to the underlying semantics of its computation. This proposal develops the thesis that interpretability is fundamentally an identifiability problem, and shows how the two fields can advance each other: both seek precise, testable links between latent variables and observable phenomena, aligning with the broader scientific method of uncovering stable and reproducible explanations.