Graph Estimation

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CIS Colloquium, Apr 17, 2013, 11:00AM – 12:00PM, Wachman 447

Graph Estimation

Han Liu, Princeton University

The graphical model has proven to be a useful abstraction in statistics and machine learning. The starting point is the graph of a distribution. While often the graph is assumed given, we are interested in estimating the graph from data. In this talk we present new nonparametric and semiparametric methods for graph estimation. The performance of these methods is illustrated and compared on several real and simulated examples.

Dr. Han Liu is an assistant Professor of Operations Research and Financial Engineering at the Princeton University. He also has a joint appointment at the Biostatistics and Computer Science Departments at the Johns Hopkins University. In 2011, he received a joint a PhD in Statistics and Machine Learning from the Machine Learning department at the Carnegie Mellon University. His thesis advisors are John Lafferty and Larry Wasserman.