Campuses:

Applied statistics

Monday, June 24, 2013 - 9:00am - 10:30am
Bin Yu (University of California, Berkeley)
Boosting is one of the two most successful machine learning methods
with SVM. It uses gradient descent to an empirical loss function.
When the step sizes are small, it is computationally efficient way
to approximate Lasso. When a nuclear norm penalization is applied to L2
loss,
we have the low-rank regularization arising from the Netflix competition.
A subset of the netflix data will be investigated.
Wednesday, June 19, 2013 - 11:00am - 12:30pm
Bin Yu (University of California, Berkeley)
This lecture will discuss variants and extenstions of Lasso
such as Lasso+LS, adaptive Lasso, and group Lasso.
Wednesday, February 15, 2012 - 10:15am - 11:15am
Sriram Sankararaman (Harvard Medical School)
Statistical power to detect associations in genome-wide association studies can be enhanced by combining data across studies in meta-analysis or replication studies. Such methods require data to flow freely in the scientific community, however, and this raises privacy concerns.
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