[Ml-stat-talks] Princeton Optimization Seminar - Po-Ling Loh, UPenn, TODAY 4:30 at Sherrerd 101

Xingyuan Fang, Ethan ethanfangxy at gmail.com
Thu Mar 26 09:02:11 EDT 2015

FYI... This should be of interests..

  *-----   **Princeton Optimization Seminar**   -----*

DATE:  Thursday, March 26 2015

TIME:  4:30 pm

LOCATION:  Sherrerd Hall 101

SPEAKER:  Po-Ling Loh, University of Pennsylvania

TITLE:  PDW Methods for Support Recovery in Nonconvex High-dimensional


The primal-dual witness (PDW) technique is a now-standard proof strategy
for establishing variable selection consistency for sparse high-dimensional
estimation problems when the objective function and regularizer are convex.
The method proceeds by optimizing the objective function over the parameter
space restricted to the true support of the unknown vector, then using a
dual witness to certify that the resulting solution is also a global
optimum of the unrestricted problem. We present a modified primal-dual
witness framework that may be applied even to nonconvex, penalized
objective functions that satisfy certain regularity conditions. Notably,
our theory allows us to derive rigorous support recovery guarantees for
local and global optima of regularized regression estimators involving
nonconvex penalties such as the SCAD and MCP, which do not involve the
restrictive incoherence conditions from Lasso-based theory. Projected
gradient descent methods may be used to obtain these optima in an efficient

Po-Ling Loh is an assistant professor in the statistics department at the
Wharton School at the University of Pennsylvania. She received a PhD in
statistics from Berkeley in 2014 and a BS in math with a minor in English
from Caltech in 2009. Po-Ling is interested in problems in high-dimensional
statistics. She is also interested in nonconvex optimization techniques and
how they may be used to promote sparsity and increase robustness.

Xingyuan Fang, Ethan
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