Review for NeurIPS paper: Sub-sampling for Efficient Non-Parametric Bandit Exploration

Additional Feedback: The authors propose the SDA approach, which has the potential to be an alternative to UCB and Thompson sampling for bandit learning. The main advantage of the proposed approach is that it is non-parametric, and for exponential families of distritions, it could achieve the optimal regret bound matching the lower bound, without knowing which distribution family the unknown distribution belongs to. However, I feel that achieve optimal regret guarantee is mostly theoretical interest. UCB may not be optimal in this sense but it could achieve consistent regret gurantee for all distributions. RB-SDA algorithm, it is unclear to me if its theoretical guarantee of O(log T) regret bound could be achieved for any distribution.