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DTSTART;TZID=Europe/Stockholm:20190612T153000
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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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