I am excited to be organizing a minimymposium on low-rank patterns in machine learning and numerical analysis at the SIAM Conference on Applied Linear Algebra 2024.

The size of data and model parameters is growing enormously in modern science and engineering. While recent advancements in computational hardware make it tempting to handle large-scale problems by merely allocating increasing resources, a much more efficient approach combines efficient hardware with techniques from model order reduction. Among the successful approaches, those leveraging low-rank formats are particularly interesting due to their ability to combine favorable memory and computational footprints with solid mathematical analysis and interpretation.

As low-rank data naturally emerge in diverse settings, including complex and quantum systems, high-dimensional optimization, and deep learning, the analysis of low-rank structures in modern systems is being pushed forward simultaneously by many different scientific areas, highlighting the fundamental importance of this type of reduced-order structure in understanding and solving complex problems.

This minisymposium brings together contributions from different communities working on this topic, with the goal of sampling recent developments in low-rank techniques with a specific focus on their relevance and application to machine learning.

Invited Speakers

• Alex Cayco-Gajic, École Normale Supérieure Paris, France
• Hung-Hsu Chou, Technische Universität München, Germany
• Romain Cosson, Inria, France
• Hessam Babaee, University of Pittsburgh, U.S.
• Mathis Dus, ENPC, France
• Filippo Vicentini, École Polytechnique, France
• Jörg Nick, ETH Zurich, Switzerland
• Emanuele Zangrando, Gran Sasso Science Institute, Italy