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Showing 1 to 1 of 1 for “"identfifiability"”.

  1. Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment

    This thesis brings together ideas from causality and representation learning. Causal models provide rich descriptions of complex systems as sets of mechanisms by which each variable is influenced by its direct causes. They support reasoning about manipulating parts of the system, capture a whole …

    cambridge Repository record for Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment (opens in a new tab)