Achieving desirable loss distributions by design

Abstract

In this work, we are interested in studying the potential of learning algorithm design that is driven by novel, diverse notions of "risk" that go well beyond the traditional choice of expected loss. In particular, we look the impact that introducing a generalized, scalable, bidirectional dispersion term has on how risk is measured, and the repercussions it has in the dynamic setting of stochastic optimization.