Abstract
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.
Sanjay Jain is supported in part by the NUS grant number RP3992710.
Frank Stephan is supported by the Deutsche Forschungsgemeinschaft (DFG) Heisenberg grant Ste 967/1-1; part of the work was done while he visited the University of Wisconsin - Madison.
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Jain, S., Stephan, F. (2003). A Tour of Robust Learning. In: Computability and Models. The University Series in Mathematics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0755-0_9
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