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AIC Seminar Series

Entropy-Driven Online Active Learning for Interactive Calendar Management

Julie WeberUniversity of Michigan[Home Page]

Notice:  hosted by Pauline Berry

Date:  2007-01-18 at 16:00

Location:  EJ228  (Directions)

   Abstract

We present a new algorithm for active learning embedded within an interactive calendar management system that learns its users’ scheduling preferences. When the system receives a meeting request, the active learner selects a set of alternative solutions to present to the user; learning is then achieved by noting the user’s preferences for the selected schedule over the others presented. To achieve the goals of presenting solutions that meet the user’s needs while enhancing the preference-learning process, we introduce a new approach to active learning that makes online decisions about the technique to use in selecting the schedules to present in response to each meeting request. The decision is based on the entropy of the available options: a highly diverse set of possible solutions calls for a selection technique that chooses instances that are different from one another, maximizing coarse-grained learning, whereas a set of possible solutions containing little diversity is met with a selection strategy that promotes fine-grained learning. We present experimental results that indicate that our entropy-driven approach provides a better balance between learning efficiency and user satisfaction than static selection techniques.

   Bio for Julie Weber

Julie S. Weber is a doctoral candidate at the University of Michigan working with Professor Martha E. Pollack in the Artificial Intelligence Laboratory in the area of Adapative Interaction. She received her Masters degree at Tufts University in 2004 and her Bachelors degree at Wellesley College in 2003. Her other research interests lie in assistive technology for the elderly and cognitively impaired, and outside of research she enjoys playing recreational soccer and frisbee. Last summer Julie interned at Google in New York where she applied machine learning techniques to sentiment analysis on the web. She is looking forward to teaching computer science to young students this summer at Michigan’s Camp Caen.

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