Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: <lb>This work addresses the problem of regret minimization in non-stochastic multi-<lb>armed bandit problems, focusing on performance guarantees that hold with high<lb>probability. Such results are rather scarce in the literature since proving them re-<lb>quires a large deal of technical effort and significant modifications to the standard,<lb>more intuitive algorithms that come only with guarantees that hold on expectation.<lb>One of these modifications is forcing the learner to sample arms from the uniform<lb>distribution at least (<lb><lb>T ) times over T rounds, which can adversely affect per-<lb>formance if many of the arms are suboptimal. While it is widely conjectured that<lb>this property is essential for proving high-probability regret bounds, we show in<lb>this paper that it is possible to achieve such strong results without this undesirable<lb>exploration component. Our result relies on a simple and intuitive loss-estimation<lb>strategy called Implicit eXploration (IX) that allows a remarkably clean analy-<lb>sis. To demonstrate the flexibility of our technique, we derive several improved<lb>high-probability bounds for various extensions of the standard multi-armed bandit<lb>framework. Finally, we conduct a simple experiment that illustrates the robustness<lb>of our implicit exploration technique.
Student:
Explore no more: Improved high-probability regret bounds for non-stochastic bandits