活动地点：

活动类型：学术报告

主讲人：郁彬 (UC Berkeley 教授)

活动时间：

活动内容：

 

Abstract:
Machine learning has been at the frontier of modern statistics
because of its serious consideration of computation. Machine
learning algorithms such as boosting and support vector machines
have shown impressive successes in prediction for large data sets.
Another important goal of statistics, in addition to prediction,
is interpretation. Now much attention is paid on L1 penalized
empirical minimizations because of the sparsity in the models
induced by the L1 penalty and sparisty is a good proxy for
interpretation.

In this talk, I would like to give an overview of recent results
by my group on research related to L1 penalized minimization.
In particular, an approximate Lasso algorithm, BLasso, is proposed
and related to e-Boosting; an irrepresentable condition is introduced
for Lasso to be model selection consistent and the consequence of
this condition's relaxation is studied; finally, I will briefly cover
a new penalization framework, CAP, for grouped and hierachical
selection of predictors, and its fast implementation.

www.stat.berkeley.edu/~binyu