快速身体活动的建议：移动健康中的高效超参数学习（CS）

玛丽安·梅尼塔斯， 萨比娜·汤姆金斯，苏珊·墨菲

可以通过在移动设备上提供相关且及时的建议，来支持用户采取健康的行为，如定期的体育活动。最近，人们发现强化学习算法对于学习提供建议的最佳文章是有效的。但是，这些算法不一定为移动健康 （mHealth） 设置构成的限制而设计，因为它们效率高、域信息高且计算实惠。我们提出了一种在移动健康环境中提供身体活动建议的算法。利用域科学，我们制定了一个利用线性混合效应模型的上下文土匪算法。然后，我们引入了一个过程，以有效地执行超参数更新，使用的计算资源比竞争方法少得多。我们的方法不仅计算效率高，还可通过闭合形式矩阵代数更新轻松实现，并且我们在速度和精度方面分别表现出高达99%和56%的特性。

Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health

Marianne Menictas, Sabina Tomkins, Susan Murphy

Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning algorithms have been found to be effective for learning the optimal context under which to provide suggestions. However, these algorithms are not necessarily designed for the constraints posed by mobile health (mHealth) settings, that they be efficient, domain-informed and computationally affordable. We propose an algorithm for providing physical activity suggestions in mHealth settings. Using domain-science, we formulate a contextual bandit algorithm which makes use of a linear mixed effects model. We then introduce a procedure to efficiently perform hyper-parameter updating, using far less computational resources than competing approaches. Not only is our approach computationally efficient, it is also easily implemented with closed form matrix algebraic updates and we show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.