ZIN: When and How to Learn Invariance Without Environment Partition?. (arXiv:2203.05818v2 [cs.LG] UPDATED)

It is commonplace to encounter heterogeneous data, of which some aspects of
the data distribution may vary but the underlying causal mechanisms remain
constant. When data are divided into distinct environments according to the
heterogeneity, recent invariant learning methods have proposed to learn robust
and invariant models based on this environment partition. It is hence tempting
to utilize the inherent heterogeneity even when environment partition is not
provided. Unfortunately, in this work, we show that learning invariant features
under this …

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