Summary of Imprecise Multi-armed Bandits, by Vanessa Kosoy
Imprecise Multi-Armed Bandits
by Vanessa Kosoy
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework combines multi-armed bandit methods with credal sets, leveraging known classes of hypotheses to model uncertainty in outcomes. This novel approach defines a notion of regret based on lower prevision and applies to two-player zero-sum games, where the agent chooses arms and the adversary selects distributions over outcomes. For specific hypothesis classes, an algorithm is proposed and an upper bound on regret established. In addition, lower bounds on regret are proven for special cases, including stochastic linear bandits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to solve multi-armed bandit problems by using unknown credal sets instead of single outcomes. This helps when the possible outcomes are complex or varied. The researchers define what it means to “lose” in this situation and propose an algorithm that does well in certain cases. They also show that there’s a limit to how well any algorithm can do. |
