Two of the biggest puzzles in machine learning are: Why is it so successful and why is it quite fragile? This talk will present a framework for unraveling these puzzles from the perspective of approximating functions in high dimensions. We will discuss what's known and what's not known about the approximation generalization properties of neural network type of hypothesis space as well as the dynamics and generalization properties of the training process. We will also discuss the relative merits of shallow vs. deep neural network models and suggest ways to formulate more robust machine learning models.  This is joint work with Chao Ma, Stephan Wojtowytsch and Lei Wu.

This is virtual seminar. A Zoom link will be published at https://www.oneworldml.org/ .