Talk 9624 data/Fall

Applied Math And Analysis Seminar
Monday, October 3, 2016, 12:00pm, 119 Physics
Cynthia Rudin (Duke University)
1) Regulating Greed Over Time: An Important Lesson For Practical Recommender Systems and 2) Prediction Uncertainty and Optimal Experimental Design for Learning Dynamical Systems

I will present work from these two papers: 1) Regulating Greed Over Time. Stefano Traca and Cynthia Rudin. 2015 Finalist for 2015 IBM Service Science Best Student Paper Award 2) Prediction Uncertainty and Optimal Experimental Design for Learning Dynamical Systems. Chaos, 2016. Benjamin Letham, Portia A. Letham, Cynthia Rudin, and Edward Browne. There is an important aspect of practical recommender systems that we noticed while competing in the ICML Exploration-Exploitation 3 data mining competition. The goal of the competition was to build a better recommender system for Yahoo!'s Front Page, which provides personalized new article recommendations. The main strategy we used was to carefully control the balance between exploiting good articles and exploring new ones in the multi-armed bandit setting. This strategy was based on our observation that there were clear trends over time in the click-through-rates of the articles. At certain times, we should explore new articles more often, and at certain times, we should reduce exploration and just show the best articles available. This led to dramatic performance improvements. As it turns out, the observation we made in the Yahoo! data is in fact pervasive in settings where recommender systems are currently used. This observation is simply that certain times are more important than others for correct recommendations to be made. This affects the way exploration and exploitation (greed) should change in our algorithms over time. We thus formalize a setting where regulating greed over time can be provably beneficial. This is captured through regret bounds and leads to principled algorithms. The end result is a framework for bandit-style recommender systems in which certain times are more important than others for making a correct decision. If time permits I will discuss work on measuring uncertainty in parameter estimation for dynamical systems. I will present "prediction deviation," a new metric of uncertainty that determines the extent to which observed data have constrained the model's predictions. This is accomplished by solving an optimization problem that searches for a pair of models that each provide a good fit for the observed data, yet have maximally different predictions. We develop a method for estimating a priori the impact that additional experiments would have on the prediction deviation, allowing the experimenter to design a set of experiments that would most reduce uncertainty. [video]

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