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The ability to learn and adapt when playing against an adap-tive opponent requires the ability to predict the opponent's behavior. Capturing any changes in the opponent's behavior during a sequence of plays is critical to achieve positive outcomes in such an environment. We identify two new requirements that we suggest are essential for agents that learn in(More)
An agent must learn and adapt quickly when playing against other agents. This process is challenging in particular when playing in stochastic environments against other learning agents. In this paper, we introduce a fast and adaptive learning algorithm for repeated stochastic games (FAL-SG). FAL-SG utilizes lossy game abstraction to reduce the state space(More)
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing(More)
Models for predicting the risk of cardiovascular (CV) events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restrict the predictive power(More)
People's driving behavior patterns have significant effects on the modern transportation systems. Public safety, traffic congestions, driving convenience are all affected by the driver's behaviors on the road. With the recent developments in data communications, streaming and mining technologies, developing driving behavior monitoring systems that can(More)
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