Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection
研究高维线性模型下的随机上下文赌博机问题，针对有限随机动作空间，改进LinUCB算法并采用最佳子集选择方法，实现了与维度几乎无关的遗憾上界，并通过实验验证了算法的适用性和鲁棒性。
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 algorithm in doubly growing epochs and estimate the parameter using the best subset selection method, which is easy to implement in practice. This approach achieves O(sT) regret with high probability, which is nearly independent of the “ambient” regression model dimension d. We further attain a sharper O(sT) regret by using the SupLinUCB framework and match the minimax lower bound of the low-dimensional linear stochastic bandit problem. Finally, we conduct extensive numerical experiments to empirically demonstrate our algorithms’ applicability and robustness. Supplementary materials for this article are available online.