DEPARTMENT OF STATISTICS
Statistics Colloquium
University of Connecticut
Storrs, Connecticut
The Department of Statistics Cordially invites you to a Colloquium
Ruoqing Zhu
Postdoctoral Associate
Department of Biostatistics
Yale University
Reinforcement learning trees: improving tree-based
methods in high-dimensional data analysis
ABSTRACT
Ensemble tree-based methods, such as random forests, are among the state-of-the-art machine
learning tools for classification, regression and other statistical modeling problems. However,
some intrinsic mechanisms in the tree construction processes have limited the performance of tree-
based methods in the ‘small-n-large-p" paradigm. Reinforcement learning trees (RLT) is
introduced to overcome these limitations. This new method implements an extremely greedy
splitting rule to pursue signals. A variable muting procedure is also proposed such that the
constructed trees are much sparser than those in traditional tree-based methods in high-
dimensional settings. The asymptotic properties of the proposed method are investigated. We
discuss the potential applications of this method in cancer genetic studies, personalized medicine,
and other related fields.
DATE: Wednesday, February 25, 2015
TIME: 4:00 p.m.
PLACE: Philip E. Austin Building – Room 105
Coffee will be served at 3:30 in room 326
For more information, contact: Tracy Burke at tracy.burke@uconn.edu