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UID:submissions.pasc-conference.org_PASC19_sess145@linklings.com
SUMMARY:MS12 - Parallel High-Dimensional Approximation: Uncertainty Quanti
 fication and Machine Learning, Part II of II
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Chemi
 stry and Materials, Physics, Engineering\n\nLarge-Scale Sparse Inverse Cov
 ariance Matrix Estimation and its Applications\n\nBollhöfer, Schenk, Eftek
 hari, Scheidegger\n\nThe estimation of large sparse inverse covariance mat
 rices is an ubiquitous statistical problem in many application areas such 
 as mathematical finance or geology or many others. Numerical approaches ty
 pically rely on the maximum likelihood estimation or its negative log-like
 lihood function. When the...\n\n---------------------\nExploiting Sparsity
  in the Estimation of Gaussian Models at Large Scales\n\nTreister\n\nThe G
 aussian distribution is one of the most fundamental statistical tools for 
 modeling data in various applications. However, estimating full covariance
  matrices is both prohibitively expensive and over-parametrized at large s
 cales. In this talk I will discuss how to exploit sparsity to overcome bo.
 ..\n\n---------------------\nH-Matrix Accelerated Second Moment Analysis f
 or Potentials with Rough Correlation\n\nDölz, Multerer, Harbrecht\n\nWe co
 nsider the efficient solution of linear operator equations with random rig
 ht-hand side. The solution's two-point correlation can efficiently be comp
 uted by means of a sparse grid or a low-rank approximation if the two-poin
 t correlation of the right-hand side is sufficiently smooth. Unfortunatel.
 ..\n\n---------------------\nParallelized High-Dimensional Sparse Inverse 
 Covariance Matrix Estimation\n\nEftekhari, Bollhöfer, Schenk\n\nWe conside
 r the problem of estimating sparse inverse covariance matrices for high-di
 mensional datasets using the l1-regularized Gaussian maximum likelihood me
 thod. This problem is particularly challenging as the required computation
 al resources increase superlinearly with the number of random variab...\n
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