Can PAC learning algorithms tolerate random attribute noise?
This paper studies the robustness of PAC learning algorithms when the instance space is {0,1}n, and the examples are corrupted by purely random noise affecting only the attributes (and not the labels). Foruniform attribute noise, in which each attribute is flipped independently at random with the same probability, we present an algorithm that PAC learns monomials for any (unknown) noise rate less than21. Contrasting this positive result, we show thatproduct random attribute noise, where each attributei is flipped randomly and independently with its own probability pi, is nearly as harmful as malicious noise-no algorithm can tolerate more than a very small amount of such noise.