Campuses:

Inverse matrix

Wednesday, June 24, 2015 - 2:00pm - 3:30pm
Julianne Chung (Virginia Polytechnic Institute and State University)
Computing reliable solutions to inverse problems is important in many applications such as biomedical imaging, computer graphics, and security. Regularization by incorporating prior knowledge is needed to stabilize the inversion process. In this talk, we develop a new framework for solving inverse problems that incorporates probabilistic information in the form of training data. We provide theoretical results for the underlying Bayes risk minimization problem and discuss efficient approaches for solving the associated empirical Bayes risk minimization problem.
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