Beyond independent component analysis: identifiability and algorithms

Datum

29.01.2026

Zeit

15:00 - 16:00

Sprecher

Alvaro Ribot

Zugehörigkeit

Harvard University

Sprache

en

Hauptthema

Biologie

Host

Local Organisors: Nikola Sadovek, Maximilian Wiesmann, Giulio Zucal

Beschreibung

Independent Component Analysis (ICA) is a classical method for recovering latent variables with useful identifiability properties. However, full independence is a strong assumption that may not hold in many real-world settings. In this talk, I will discuss how much we can relax the independence assumption without losing identifiability of the model. We show that the weakest such assumption is pairwise mean independence. Our identifiability result is based on a generalization of the spectral theorem from matrices to higher-order tensors, which implies a unique tensor decomposition of the cumulant tensors arising in the model. This is joint work with Anna Seigal and Piotr Zwiernik.

Letztmalig verändert: 29.01.2026, 07:39:49