Mini-Workshop: High-Dimensional Control Problems and Mean-Field Equations with Applications in Machine Learning

  • Giacomo Borghi

    Heriot-Watt University, Edinburgh, UK
  • Elisa Iacomini

    Università degli Studi di Ferrara, Ferrara, Italy
  • Mathias Oster

    RWTH Aachen University, Aachen, Germany
  • Chiara Segala

    Università della Svizzera Italiana, Lugano, Switzerland
Mini-Workshop: High-Dimensional Control Problems and Mean-Field Equations with Applications in Machine Learning cover
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Abstract

High-dimensional control problems and mean field equations have been of increased interest in recent years and novel numerical tools tackling the curse of dimensionality have been developed. These optimization tasks are strongly related to learning problems such as data-driven optimal control and learning of deep neural networks. As a consequence, there is a huge potential to employ control theoretical techniques in Machine Learning. The Mini-Workshop was devoted to discuss possible synergies among the various tools developed in these fields.

Cite this article

Giacomo Borghi, Elisa Iacomini, Mathias Oster, Chiara Segala, Mini-Workshop: High-Dimensional Control Problems and Mean-Field Equations with Applications in Machine Learning. Oberwolfach Rep. 21 (2024), no. 4, pp. 3211–3254

DOI 10.4171/OWR/2024/56