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DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. (arXiv:2208.01373v1 [cs.LG])
Aug. 3, 2022, 1:11 a.m. | Yanke Li, Hatt Tobias, Ioana Bica, Mihaela van der Schaar
stat.ML updates on arXiv.org arxiv.org
Leveraging labelled data from multiple domains to enable prediction in
another domain without labels is a significant, yet challenging problem. To
address this problem, we introduce the framework DAPDAG (\textbf{D}omain
\textbf{A}daptation via \textbf{P}erturbed \textbf{DAG} Reconstruction) and
propose to learn an auto-encoder that undertakes inference on population
statistics given features and reconstructing a directed acyclic graph (DAG) as
an auxiliary task. The underlying DAG structure is assumed invariant among
observed variables whose conditional distributions are allowed to vary across
domains led …
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