I'm wondering if there's an algorithm that minimizes the expected posterior loss for the best performing bandit where regret is calculated as the number of trials to achieve a threshold for posterior loss.
Example: Let's say we run an AB test to compare click rates for 3 different creatives. For each user, we can decide which creative she will get. Since we are just pretending this is a real-world scenario, we don't gain anything from the click - but we have to pay for each impression the same. Is there a way (an algorithm) to find which creative performs the best in the least amount of impression (trials)?