Summary of Stochastic Rising Bandits, by Alberto Maria Metelli et al.
Stochastic Rising Bandits
by Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli
First submitted to arxiv on: 7 Dec 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper presents two new algorithms for stochastic Multi-Armed Bandits (MABs) in scenarios where the expected payoff is monotonically non-decreasing. The rested and restless cases are addressed, with designed algorithms R-ed-UCB and R-less-UCB providing tight regret bounds of (T^{}) under certain conditions. Experimental evaluations on synthetic tasks and a real-world dataset demonstrate the effectiveness of these approaches compared to state-of-the-art methods for non-stationary MABs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best option in a series of choices, where each choice gives us feedback. The goal is to make good decisions quickly and learn from our mistakes. The researchers created special algorithms to help with this problem when the options get better over time. They tested these algorithms on fake data and real-world problems, showing that they work well compared to other methods. |
