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

Estimation with Norm Regularization, with Applications to Climate Science

Arindam Banerjee,

Date and Time: Oct 08, 2014 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building


The talk will discuss recent advances in the analysis of
non-asymptotic estimation error and structured statistical recovery
based on norm regularized regression, such as Lasso, as well as
application of such estimation to climate science.

Analysis of estimation error for regularized problems needs to
consider four aspects: the norm, the loss function, the design
matrix, and the noise model. The talk will discuss new results on
all four aspects. In particular, the new results are applicable to
any norm, general design matrices, including sub-Gaussian,
anisotropic, and dependent samples, general convex loss functions,
including least squares and generalized linear models, and both
Gaussian and sub-Gaussian noise models. Gaussian width, a measure
of size of sets, and associated tools play a key role in our
general analysis. We show applications of the such
structured/sparse estimation for multi-task learning in the context
of combining climate models, with promising preliminary results.