PAC Generalization via Invariant Representations. (arXiv:2205.15196v3 [cs.LG] UPDATED)

One method for obtaining generalizable solutions to machine learning tasks
when presented with diverse training environments is to find \textit{invariant
representations} of the data. These are representations of the covariates such
that the best model on top of the representation is invariant across training
environments. In the context of linear Structural Equation Models (SEMs),
invariant representations might allow us to learn models with
out-of-distribution guarantees, i.e., models that are robust to interventions
in the SEM. To address the invariant representation …

arxiv lg