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Session

OT2: Optimal Transport: Theory and Applications Location: G227 Session Chair: Stephan Eckstein Session Chair: Johannes Wiesel

Presentation 1

On the role of Rademacher complexities in statistical learning Daniel Bartl NUS, Singapore We study the problem of learning with respect to the squared loss over a convex class of functions. It has long been believed that the sample complexity in this setting is governed by localized Rademacher complexities. We show that, assuming access to coarse information on the covariance structure of the model class, the sample complexity is instead controlled by a localized complexity associated with the limiting Gaussian process. In heavy-tailed regimes, this quantity can be significantly smaller than the Rademacher complexity.