On Learning High-Dimensional Sparse Structured Input-Output Models, with Applications to Genome-Phenome Association Analysis of Complex Diseases and Web-Scale Image Understanding

Abstract:

 

In many modern problems across areas such as genomics, computer vision, and natural language process, one is interested in learning a Sparse Structured Input-Output Regression Model (SIORM), in which the input variables of the model such as variations on a human genome bear rich structure due to the genetic and functional dependences between entities in the genome; and the output variables such as the disease traits are also structured because of their interrelatedness. A SIORM can nicely capture rich structural properties in the data, but raises severe computational and theoretical challenge on consistent model identification. 

 

In this talk, I will present models, algorithms, and theories that learn Sparse SIORMs of various kinds in very high dimensional input/output space, with fast and highly scalable optimization procedures, and strong statistical guarantees. I will demonstrate application of our approach to problems in large-scale genome association analysis and web image understanding.

 

This is joint work with Seyoung Kim, Xi Chen, Seunghak Lee, and Bin Zhao.

 

 

About the speaker:

 

Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models; 2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and 3) application of statistical learning in social networks, data mining, vision. Professor Xing has published over 140 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics, the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning journal. He is a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, and the United States Air Force Young Investigator Award.