CAREER: Efficient Learning of Personalized Strategies

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Online retailers frequently provide tailored product or movie recommendations. But the power of automated personalization, driven by data and statistics, could be far greater: imagine the impact on poverty reduction if all children had a personalized, self-improving tutoring system as part of their education. To realize this vision requires personalization systems that reason about both the immediate impact of a recommended item (e.g. will a learner immediately learn from a video lecture) as well as its longer term impact. For example, a recommended item or intervention may cause a user to change his/her preferences, state of knowledge, or reveal information about the user that was previously unknown. This requires methods for creating personalized strategies: adaptive rules about what decisions to make (whether or which ad to show, which pedagogical activity to provide) in which circumstances to maximize for long term outcomes. This research involves developing new data-driven, machine learning approaches to construct such personalized strategies for related individuals, and using them towards improving the effectiveness of online mathematics educational systems. The project frames personalized strategy creation as sequential decision making under uncertainty research. Though there have been many advances in sequential decision making under uncertainty, existing approaches have focused primarily on other application areas, like robotics, and fail to account or leverage for some of the special features that arise when interacting with people. These include that accurate simulation of people is difficult but prior data is often available, and that individuals are often related. This project contributes algorithms for mining existing datasets to create and precisely bound the expected performance of new high-quality strategies and for online policy learning across a series of similar sequential decision making tasks.