062ddb6c727310e76b6200b7c71f63b5-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers transfer learning in a multi-armed bandit setting. The model considered has a sequence of episodes, and in each episode, the vector of distributions (one for each arm) is drawn iid from a discrete distribution. In this setting, it is possible to exploit history to learn what this discrete distribution is, and to use this information to reduce regret in each episode. An algorithm is proposed that does this, and cumulative regret bounds are shown for this algorithm.