Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments

Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the o...

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Hlavní autoři:	Hang Su, Wei Wang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2024-02-01
Edice:	Mathematics
Témata:	invariant feature learning causal representation learning out-of-distribution generalization causal inference
On-line přístup:	https://www.mdpi.com/2227-7390/12/5/696
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