Monday, October 19, 2015 - 4:00pm to 4:50pm
KEC 1003

Speaker Information

Daniel Lowd
Assistant Professor
Department of Computer and Information Science
University of Oregon

Abstract

Probabilistic graphical models have been applied to many domains, including computer vision, natural language processing, and bioinformatics. However, their effectiveness is limited by the complexity of inference, which is generally intractable. An appealing alternative is to work with tractable probabilistic models, in which exact inference is efficient. Sum-product networks (SPNs) are a deep, tractable probabilistic representation that generalize many other tractable model classes. SPNs have achieved state-of-the-art results on computer vision and density estimation problems, but selecting a good structure for an SPN is challenging.

In this talk, I will provide a brief introduction to SPNs and then discuss several recent approaches to learning SPN structures from data. The first approach is to adapt standard graphical model structure learning algorithms, resulting in SPNs that represent tractable graphical models. The second approach is to recursively cluster instances and variables, resulting in SPNs that represent hierarchical mixture models. These two approaches capture different types of patterns in the data. The ID-SPN algorithm uses a combined approach, leading to much better structures on a variety of benchmark domains. In many cases, ID-SPN learns SPNs that are more accurate than intractable Bayesian networks, demonstrating that SPNs can maintain tractability without sacrificing accuracy.

Speaker Bio

Daniel Lowd is an Assistant Professor in the Department of Computer and Information Science at the University of Oregon. His research interests include learning and inference with probabilistic graphical models, adversarial machine learning, and statistical relational machine learning. He received his Ph.D. in 2010 from the University of Washington. He has received a Google Faculty Award in 2013, an ARO Young Investigator Award in 2015, and the best paper award at DEXA 2015.