A Tour of Robust Learning

Bārzdiņš conjectured that only recursively enumerable classes of functions can be learned robustly. This conjecture, which was finally refuted by Fulk, initiated the study of notions of robust learning. The present work surveys research on robust learning and focuses on the recently introduced variants of uniformly robust and hyperrobust learning. Proofs are included for the (already known) results that uniformly robust Ex-learning is more restrictive than robust Ex-learning, that uniformly robustly Ex-learnable classes are consistently learnable, that hyperrobustly Ex-learnable classes are in Num and that some hyperrobustly BC-learnable class is not in Num.