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Mechanisms for generalized learning/Personalized HeartSteps

Natasha Jaques
MIT Media Lab
Mechanisms for generalized learning across tasks and environments

Abstract: Current approaches to machine learning may often involve tuning an algorithm to perform well on a specific task, and as such do not represent a general method for learning that could be valuable across many different scenarios. This talk will cover a range of techniques for addressing this problem, including multi-task learning, transfer learning, intrinsic motivation in reinforcement learning, and learning from human preferences. We show how multi-task learning can be used to account for a large degree of heterogeneity between individuals and improve performance in predicting mental health outcomes. Transfer learning can be used to combine training on data with reinforcement learning, to both reduce catastrophic forgetting and improve drug discovery algorithms. Finally, I argue that social learning is an important intrinsic motivator, and show how it can be used in both multi-agent systems and to learn from implicit human preferences.

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Susan Murphy
Harvard SEAS and Statistics

Personalized HeartSteps: A reinforcement learning algorithm for optimizing physical activity
Abstract: A formidable challenge in designing sequential treatments is to determine when and in which context it is best to deliver treatments. Consider treatment for individuals struggling with chronic health conditions. Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment. That is, the treatment is adapted to the individual's context; the context may include current health status, current level of social support and current level of adherence for example. Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules. There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment. Here we discuss our work to design a reinforcement learning algorithm for use in optimizing physical activity using mobile health.