Robust Representation Learning through Explicit Environment Modeling

ArXi:2604.26128v1 Announce Type: cross We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This problem is commonly approached from a causal perspective, seeking invariant representations that retain causal factors while discarding spurious ones. However, this framework assumes that the environment has no direct effect on the target.