The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group


with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

A data-dependent weighted LASSO

Rebecca Willett, Prof., Department of Electrical and Computer Engineering, UW-Madison

Date and Time: Aug 20, 2015 ( 4:00 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


Sparse linear inverse problems appear in a variety of settings, but
often the noise contaminating observations cannot accurately be
described as bounded or arising from a Gaussian distribution. Poisson
observations in particular are a characteristic feature of several
real-world applications. Previous work on sparse Poisson inverse
problems encountered several limiting technical hurdles. I will
describe an alternative, streamlined analysis approach for sparse
Poisson inverse problems which (a) sidesteps the technical challenges
present in previous work, (b) admits estimators that can readily be
computed using off-the-shelf LASSO algorithms, and (c) hints at a
general weighted LASSO framework for broader classes of problems. At
the heart of this new approach lies a weighted LASSO estimator for
which data-dependent weights are based on Poisson concentration
inequalities. Unlike previous analyses of the weighted LASSO, the
proposed analysis depends on conditions which can be checked or shown
to hold in general settings with high probability.