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cs.LG2025
Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
Zhiwei Han, Stefan Matthes, Hao Shen
In this work, we establish the sufficient conditions under which nonlinear Canonical Correlation Analysis (CCA) recovers ground-truth latent factors up to an affine transformation.…
cs.LG2025
Mechanistic Independence: A Principle for Identifiable Disentangled Representations
Stefan Matthes, Zhiwei Han, Hao Shen
Disentangled representations seek to recover latent factors of variation underlying observed data, yet their identifiability is still not fully understood. We introduce a unified f…