Summary of Rising Rested Bandits: Lower Bounds and Efficient Algorithms, by Marco Fiandri et al.
Rising Rested Bandits: Lower Bounds and Efficient Algorithms
by Marco Fiandri, Alberto Maria Metelli, [Francesco Trovo](https://groovesquid.com/?s=Francesco+Trovo)
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach to stochastic Multi-Armed Bandits (MABs), specifically rested bandits, is presented in this paper. The authors investigate the sample complexity of regret minimization by deriving lower bounds for this problem. They then design an algorithm, R-ed-UCB, which achieves a regret bound dependent on instance properties and, under certain conditions, O(T^(2/3)) scaling. This work compares favorably to state-of-the-art methods for non-stationary MABs in synthetically generated tasks and an online model selection problem using real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can make good decisions by trying different things and learning from the results. The authors are studying a special kind of decision-making called rested bandits, where the options get better over time. They figured out that some algorithms are really good at making these kinds of decisions, especially when they’re used for tasks like selecting models or choosing what to do next. |
