Partial Identifiability for Domain Adaptation

Understand

Unsupervised domain adaptation is critical to many real-world applications where label information is unavailable in the target domain.

• In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.
• To address this issue, we rely on the property of minimal changes of causal mechanisms across domains to minimize unnecessary influences of distribution shifts.
• To encode this property, we first formulate the data-generating process using a latent variable model with two partitioned latent subspaces: invariant components whose distributions stay the same across domains and sparse changing components that vary across domains.