Overparametrization, Regularization, Identifiability and Uncertainty in Machine Learning

Abstract

In machine learning, a field addressing the extraction of information and structure from finite data with the means of computer science and mathematics, maps from finite-dimensional spaces of data or computations into spaces of higher, or infinite dimensionality are a central theme. The workshop brought together researchers with diverse viewpoints to discuss how different theoretical sub-communities within the field treat the resulting ill-posed operations, and what kind of features of algorithms and models can emerge as a result.