Efficiently Learning Probabilistic Graphical Models
Probabilistic graphical models are used to represent uncertainty in many domains, such as error-correcting codes, computational biology, sensor networks and medical diagnosis. This talk will discuss two approaches to the problem of learning graphical models from data, focusing on computational challenges. The first is marginalization-based learning, where parameters are fit in the context of a specific approximate inference algorithm. This will include results on image processing and computer vision problems. The second is recent work on Markov chain Monte Carlo based learning, inspired by a computational biology project.
Justin Domke received a Bachelor's degrees in Physics and Computer Science from Washington University in Saint Louis in 2002. After spending a year working in the Anesthesiology department at Johns Hopkins Medical School, he received a Master's degree and Ph.D. in Computer Science from the University of Maryland, College Park in 2005 and 2009. Since 2009 he has been an Assistant Professor in the College of Computing and Information Sciences at Rochester Institute of Technology.