The power of least squares in the worst-case and learning setting

 Speaker: Felix Bartel (Technische Universität Chemnitz)

Least squares approximation is a time-tested method for function approximation based on samples. It is a natural to compare it to approximations based on arbitrary linear functionals as a benchmark. In this talk we will present recent results from the information-based complexity community showing the optimality of the least squares algorithm. We will consider the worst-case setting: drawing points which are good for a class of functions and the learning setting: we approximate an individual function based on possibly noisy samples. We support our findings with numerical experiments.

Time: February 24, 2023 2:30pm-3:30pm
Location: LeConte 440