Events Calendar

CS Colloq: Jesse Davis

Talk Title: Predicate Invention and Transfer LearningSpeaker: Jesse DavisHost: Prof. Gaurav Sukhatme and Prof. Craig KnoblockAbstract: Machine learning has become an essential tool for analyzing biological and clinical data, but significant technical hurdles prevent it from fulfilling its promise. Standard algorithms make three key assumptions: the training data consist of independent examples, each example is described by a pre-defined set of attributes, and the training and test instances come from the same distribution. Biomedical domains consist of complex, inter-related, structured data, such as patient clinical histories, molecular structures and protein-protein interaction information. The representation chosen to store the data often does not explicitly encode all the necessary features and relations for building an accurate model. For example, when analyzing a mammogram, a radiologist records many properties of each abnormality, but does not explicitly encode how quickly a mass grows, which is a crucial indicator of malignancy. In the first part of this talk, I will focus on the concrete task of predicting whether an abnormality on a mammogram is malignant. I will describe an approach I developed for automatically discovering unseen features and relations from data, which has advanced the state-of-the-art for machine classification of abnormalities on a mammogram. It achieves superior performance compared to both previous machine learning approaches and radiologists.In the second part of this talk, I will address the problem of generalizing across different domains. Unlike machines, humans are able take knowledge learned in one domain and apply it to an entirely different one. Computationally, the missing link is the ability to discover structural regularities that apply to many different domains, irrespective of their superficial descriptions. This is arguably the biggest gap between current learning systems and humans. I will describe an approach based on a form of second-order Markov logic, which discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. This approach has successfully transferred learned knowledge between a molecular biology domain and a Web one. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, like various forms of homophily.Bio: Jesse Davis is a post-doctoral researcher at the University of Washington. He received his Ph.D in computer science at the University of Wisconsin – Madison in 2007 and a B.A. in computer science from Williams College in 2002. His research interests include machine learning, statistical relational learning, transfer learning, inductive logic programming and data mining for biomedical domains.

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: CS Front Desk