The contemporary practice in deep learning has challenged conventional approaches to machine learning. Specifically, deep neural networks are highly overparameterized models with respect to the number of data examples and are often trained without explicit regularization. Yet they achieve state-of-the-art generalization performance. Understanding the overparameterized regime requires new theory and foundational empirical studies. A prominent recent example is the "double descent" behavior of generalization errors that was discovered empirically in deep learning and then very recently analytically characterized for linear regression and related problems in statistical learning.

The goal of this workshop is to cross-fertilize the wide range of theoretical perspectives that will be required to understand overparameterized models, including the statistical, approximation theoretic, and optimization viewpoints. The workshop concept is the first of its kind in this space and enables researchers to dialog about not only cutting edge theoretical studies of the relevant phenomena but also empirical studies that characterize numerical behaviors in a manner that can inspire new theoretical studies.