Special Data Science Seminar
Monday, February 23, 2015
11:30 am - 1:00 pm
Gross Hall 330
Cynthia Rudin, MIT CSAIL and Sloan School of Management
lunch served at 11:30am, talk begins at 12noon We often use predictive models to make a decision afterwards. For instance, we might estimate the number of patients at a medical clinic and then designate resources to serve those patients. The class of accurate predictive models might be quite large (called the "Rashomon effect" by Breiman), which leads to two observations, (i) there may be very accurate, yet very sparse logical models that are naturally useful for decision making, (ii) if the decision problem is coupled over a set of unlabeled points (like scheduling), there may be a large range of decisions resulting from the set of good predictive models. Thus in the first part of the talk I will discuss how predictive modeling can be made to help with decision-making, and in the second part, I will discuss how prior knowledge about decisions can help with prediction. The first part uses Bayesian analysis, and the second part is a statistical learning theory result.