Alternative Measures of Computational Complexity with Applications to Agnostic Learning

We address a fundamental problem of complexity theory the inadequacy of worst-case complexity for the task of evaluating the computational resources required for real life problems. While being the best known measure and enjoying the support of a rich and elegant theory, worst-case complexity seems gives rise to over-pessimistic complexity values. Many standard task, that are being carried out routinely in machine learning applications, are NP-hard, that is, infeasible from the worst-case-complexity perspective. In this work we offer an alternative measure of complexity for approximations-optimization tasks. Our approach is to define a hierarchy on the set of inputs to a learning task, so that natural (’real data’) inputs occupy only bounded levels of this hierarchy and that there are algorithms that handle in polynomial time each such bounded level.