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04.10.2022 14:00 Workshop MMSC 2022:
Workshop on Mathematical Modeling and Scientific ComputingOnline: attendVirtuelle Veranstaltung (Boltzmannstr. 3, 85748 Garching)

October 4 to 7, 2022: The workshop focuses on mathematical modeling, analysis and computer simulation of complex physical and biological processes and systems. The objective is to create an interdisciplinary workshop, which brings together international experts working on mathematical models from various fields. The aim is to get new innovative ideas and create synergies between the scientific research topics. Program: http://www-m6.ma.tum.de/~turova/html/MMSC2022_program.pdf; Details can be found at https://easychair.org/cfp/MMSC-2022.

19.10.2022 13:15 Frank Röttger (University of Geneva, Switzerland):
Graph Laplacians in StatisticsOnline: attendBC1 2.01.10 (Parkring 11, 85748 Garching)

The Laplacian matrix of an undirected graph with positive edge weights encodes graph properties in matrix form. In this talk, we will discuss how Laplacian matrices appear prominently in multiple applications in statistics and machine learning. Our interest in Laplacian matrices originates in graphical models for extremes. For Hüsler--Reiss distributions, which are considered as an analogue of Gaussians in extreme value theory, they characterize an extremal notion of multivariate total positivity of order 2 (MTP2). This leads to a consistent estimation procedure with a typically sparse graphical structure. Furthermore, the underlying convex optimization problem under Laplacian constraints allows for a simple block descent algorithm that we implemented in R. An active area of research in machine learning are Laplacian-constrained Gaussian graphical models. These models admit structure learning under various connectivity constraints. Multiple algorithms for these problems with different lasso-type penalties are available in the literature. A surprising appearance of Laplacian matrices is in the design of discrete choice experiments. Here, the Fisher information of a discrete choice design is a Laplacian matrix, which gives rise to a new approach for learning locally D-optimal designs.

26.10.2022 12:15 Helmut Farbmacher (TUM):
t.b.a.Online: attendBC1 2.01.10 (Parkring 11, 85748 Garching)

t.b.a.