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[Colloquium] Interactive Learning of Dependency Networks for Scientific Discovery

March 19, 2013

Watch Colloquium: 

M4V file (532 MB)

  • Date: Tuesday, March 19, 2013 
  • Time: 11:00 am — 11:50 am 
  • Place: Mechanical Engineering 218

Diane Oyen
UNM Department of Computer Science
PhD Student 

Machine learning algorithms for identifying dependency networks are being applied to data in biology to learn protein correlations and neuroscience to learn brain pathways associated with development, adaptation and disease. Yet, rarely is there sufficient data to infer robust individual networks at each stage of development or for each disease/control population. Therefore, these multiple networks must be considered simultaneously; dramatically expanding the space of solutions for the learning problem. Standard machine learning objectives find parsimonious solutions that best fit the data; yet with limited data, there are numerous solutions that are nearly score-equivalent. Effectively exploring these complex solution spaces requires input from the domain scientist to refine the objective function.

In this talk, I present transfer learning algorithms for both Bayesian networks and graphical lasso that reduce the variance of solutions. By incorporating human input in the transfer bias objective, the topology of the solution space is shaped to help answer knowledge-based queries about the confidence of dependency relationships that are associated with each population. I also describe an interactive human-in-the-loop approach that allows a human to react to machine-learned solutions and give feedback to adjust the objective function. The result is a solution to an objective function that is jointly defined by the machine and a human. Case studies are presented in two areas: functional brain networks associated with learning stages and with mental illness; and plasma protein concentration dependencies associated with cancer.

 

Bio: Diane Oyen received her BS in electrical and computer engineering from Carnegie Mellon University. She then worked for several years designing ethernet controller chips and teaching math before returning to academia. Currently, she is a PhD Candidate advised by Terran Lane in computer science at the University of New Mexico. Her broad research interests are in developing machine learning algorithms to aid the discovery of scientific knowledge. She has focused on using transfer learning in structure identification of probabilistic graphical models learned from data with interaction from a human expert. She has been invited to present her research at LANL and currently serves on the senior program committee of AAAI.