Testing Assumptions of Learning Algorithms

(PASSCODE: 985224).

Many algorithms in modern learning theory assume that data satisfies various distributional assumptions (such as Gaussianity or uniformity over {0,1}^d). To be confident that these algorithms give valid predictions, one needs to be confident that the assumptions these algorithms make are correct. This motivates the research direction of testing distributional assumptions of learning algorithms. This research direction studies algorithms that come together with a tester, which can alert a user if some assumptions are not satisfied. Conversely, if the tester finds a specific dataset to be satisfactory, then a user can be confident in the guarantee given by the learning algorithm.