Machine Learning NeEDS Mathematical Optimization with Prof Katya Scheinberg

Abstract: Continuous optimization is a mature field, which has recently undergone major expansion and change. One of the key new directions is the development of methods that do not require exact information about the objective function. Nevertheless, the majority of these methods, from stochastic gradient descent to «zero-th order» methods use some kind of approximate first order information. We will overview different methods of obtaining this information in different settings, including simple stochastic gradient via sampling, traditional and randomized finite difference methods and more. We will discuss what key properties of these inexact, stochastic first order oracles are useful for convergence analysis of optimization methods that use them.