EF1 – Extracting dynamical Laws from Complex Data



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

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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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