Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
通过最佳子集选择实现小行动空间下近乎维数无关的稀疏线性赌博机
We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a randomly generated contextual covariate. This setting finds essential applications such as personalized recommendations, online advertisements, and personalized medicine. However, it is very challenging to balance the exploration and exploitation tradeoff. We modify the LinUCB…