To overcome the transductive limitation, we consider a feature augmentation approach that leverages lowfidelity data to learn an approximation of the objective function in that domain. Then, transfer learning to the high-fidelity domain happens via the augmented feature map Ψ (Hybrid label) (x) = (f S (x), x) if x ∈ X S , ( fS (x), x) otherwise where fS is an approximation of the low-fidelity objective function f S . This is a hybrid approach that allows extending to the inductive setting with a different treatment between instances observed in the low-fidelity domain and the ones associated with the high-fidelity task exclusively. Another possible extension that treats all instances in the high-fidelity domain equally is via the map Ψ (Predicted label) that augments the input feature representation using an approximate low-fidelity objective ( fS ), i.e.