Summary of Best-arm Identification in Unimodal Bandits, by Riccardo Poiani et al.
Best-Arm Identification in Unimodal Bandits
by Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, Rémy Degenne
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper tackles the fixed-confidence best-arm identification problem in unimodal bandits, where arm means increase up to their maximum before decreasing. Researchers derive two lower bounds on algorithm stopping times, with instance-dependent and worst-case implications for confidence-dependent and independent costs. Building on Track-and-Stop and Top Two algorithms, modifications are proposed that exploit the unimodal structure, achieving asymptotic optimality for one-parameter exponential families and near-optimality for Gaussian distributions. Empirical performance is demonstrated, showcasing competitive results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research looks at how to choose the best option from a set of possibilities when some options are better than others in certain ways. The study finds that there are two important limits on how quickly we can make this choice while still getting good results. The researchers then suggest new approaches that work well with these limits and show they perform similarly well as other methods. |
