[Ml-stat-talks] Fwd: Wilks Statistics Seminar: Yufeng Liu, Today, September 25th at 12:30pm, Sherrerd Hall 101

Barbara Engelhardt bee at princeton.edu
Fri Sep 25 09:57:55 EDT 2015

Talk of interest today.

---------- Forwarded message ----------
From: Carol Smith <carols at princeton.edu>
Date: Fri, Sep 25, 2015 at 8:54 AM
Subject: Wilks Statistics Seminar: Yufeng Liu, Today, September 25th at
12:30pm, Sherrerd Hall 101
To: wilks-seminar at princeton.edu

***   Wilks Statistics Seminar   ***

DATE:  Today, September 25, 2015

TIME:   12:30pm

LOCATION:   Sherrerd Hall, room 101

SPEAKER:    Yufeng Liu, University of North Carolina

TITLE:  Sparse Regression Incorporating Graphical Structure Among Predictors

ABSTRACT:  With the abundance of high dimensional data in various
disciplines, sparse regularized techniques are very popular these days. In
this talk, we use the structure information among predictors to improve
sparse regression models. Typically, such structure information can be
modeled by the connectivity of an undirected graph. Most existing methods
use this graph edge-by-edge to encourage the regression coefficients of
corresponding connected predictors to be similar. However, such methods may
require expensive computation when the predictor graph has many edges.
Furthermore, they do not directly utilize the neighborhood information. In
this work, we incorporate the graph information node-by-node instead of
edge-by-edge. Our proposed method is quite general and it includes adaptive
Lasso, group Lasso and ridge regression as special cases. Both theoretical
study and numerical study demonstrate the effectiveness of the proposed
method for simultaneous estimation, prediction and model selection.
Applications to Alzheimer's disease data and cancer data will be discussed
as well.
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