Summary of Delay As Payoff in Mab, by Ofir Schlisselberg et al.
Delay as Payoff in MAB
by Ofir Schlisselberg, Ido Cohen, Tal Lancewicki, Yishay Mansour
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 paper proposes a novel variant of the stochastic Multi-armed Bandit (MAB) problem, where the payoff is delayed and corresponds to the magnitude of the delay. This setting is inspired by real-world scenarios such as routing data packets through a network or browsing web pages with varying content. The authors aim to develop an efficient algorithm for this variant, leveraging the relationship between delay and payoff to improve decision-making in uncertain environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies a special type of game called Multi-armed Bandit (MAB). In MAB, we choose from many options, trying to get the best result. But what if the result takes some time to happen? That’s exactly what this paper is about: how can we make good choices when there’s a delay in getting the outcome? The authors look at real-life examples like sending data packets or browsing websites and show that their new approach helps us make better decisions. |
