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stat.ML2021★ 7 cited
Fundamental limits for learning hidden Markov model parameters
Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat
We study the frontier between learnable and unlearnable hidden Markov models (HMMs). HMMs are flexible tools for clustering dependent data coming from unknown populations. The mode…
stat.ML2021★ 21 cited
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy +4
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to…