Machine Learning Seminar: Machine Learning for Human Decisionmaking
Wednesday, March 9, 2016
3:30 pm - 5:00 pm
Gross Hall 330
In this talk we discuss the question of developing machine learning methodology for estimating the quality of individual judges and obtaining diagnostic insights into how various judges decide on different kinds of items. We develop a series of increasingly powerful hierarchical Bayesian models, which infer latent groups of judges and items with the goal of obtaining insights into the underlying decision process. We apply our framework to a wide range of real-world domains, and demonstrate that our approach can accurately predict judge's decisions, diagnose types of mistakes judges tend to make, and infer true labels of items.
BIO: Jure Leskovec