We consider the problem of best arm identification in stochastic multi-armed bandits, in the setting that each arm is sampled once in each round. This uniform sampling regime is a conceptually simple setting that is relevant to many practical applications. The aim is to stop and correctly identify the best arm with probability at least 1 - 6, while keeping the number of rounds low. We derive a lower bound on the sample complexity for this setting. Thereafter, we propose two natural stopping rules for Bernoulli bandits: one based on PPR martingale confidence sequences, and the other based on the GLR statistic. Both rules are shown to match the lower bound as$fi \rightarrow 0$_ Our analysis and experiments suggest that the relative performance of the two stopping rules depends on a property of the bandit instance.

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Different downsampling strategy preserves different statistics of the data and have different magnitude of latency reductions. The optimum choice of sampling strategy often depends on the particular context of the analysis flow and the hidden intent of the analyst. In this paper, we are the first to consider the impact of sampling in interactive data exploration settings as they introduce approximation errors. We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. Evaluations with real datasets show that our technique can preserve the original insight generation flow while improving the interaction latency, compared to baseline methods.