Computational Limitations in Robust Classification and Win-Win Results

no code implementations • 4 Feb 2019 • Akshay Degwekar, Preetum Nakkiran, Vinod Vaikuntanathan

We continue the study of statistical/computational tradeoffs in learning robust classifiers, following the recent work of Bubeck, Lee, Price and Razenshteyn who showed examples of classification tasks where (a) an efficient robust classifier exists, in the small-perturbation regime; (b) a non-robust classifier can be learned efficiently; but (c) it is computationally hard to learn a robust classifier, assuming the hardness of factoring large numbers.

Classification General Classification +1