Rina Foygel
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction.
Tue, 02/22/2011 - 16:01 — SomNambulAuthors: Nathan Srebro, Rina Foygel
Subjects: Learning
AbstractWe consider the problem of approximately reconstructing a partially-observed,
approximately low-rank matrix. This problem has received much attention lately,
mostly using the trace-norm as a surrogate to the rank. Here we study low-rank
matrix reconstruction using both the trace-norm, as well as the less-studied
max-norm, and present reconstruction guarantees based on existing analysis on
the Rademacher complexity of the unit balls of these norms. We show how these
are superior in several ways to recently published guarantees based on
specialized analysis.- 2 comments
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Exact block-wise optimization in group lasso for linear regression.
Tue, 10/19/2010 - 15:01 — SomNambulAuthors: Mathias Drton, Rina Foygel
Subjects: Machine Learning
AbstractThe group lasso is a penalized regression method, used in regression problems
where the covariates are partitioned into groups to promote sparsity at the
group level. Existing methods for finding the group lasso estimator either use
gradient projection methods to update the entire coefficient vector
simultaneously at each step, or update one group of coefficients at a time
using an inexact line search to approximate the optimal value for the group of
coefficients when all other groups' coefficients are fixed.Global identifiability of linear structural equation models.
Mon, 03/08/2010 - 16:01 — SomNambulAuthors: Mathias Drton, Seth Sullivant, Rina Foygel
Subjects: Statistics
AbstractStructural equation models are multivariate statistical models that are
defined by specifying noisy functional relationships among random variables. We
consider the classical case of linear relationships and additive Gaussian noise
terms. We give a necessary and sufficient condition for global identifiability
of the model in terms of a mixed graph encoding the linear structural equations
and the correlation structure of the error terms.
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