EF1 – Extracting dynamical Laws from Complex Data

Project

EF1-14

Sparsity and Sample-Size Efficiency in Structured Learning

Project Heads

Sebastian Pokutta

Project Members

David Martínez-Rubio

Project Duration

01.01.2022 − 31.12.2023

Located at

ZIB

Description

In this project, we will investigate the properties of sparse solutions induced through learning algorithms that promote sparsity. In particular, we want to look at sparsity as being a consequence of model and the implicit sparsity bias of the chosen optimization method. Closely connected to this is the question of sample-size efficiency as these algorithms also tend to have a much higher sample-size efficiency by implicitly restricting the degrees of freedom.

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