Summary of Graph Feedback Bandits with Similar Arms, by Han Qi et al.
Graph Feedback Bandits with Similar Arms
by Han Qi, Guo Fei, Li Zhu
First submitted to arxiv on: 18 May 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 investigates the stochastic multi-armed bandit problem with graph feedback, motivated by clinical trials and recommendation systems. The authors establish a regret lower bound for this novel feedback structure and propose two UCB-based algorithms: D-UCB and C-UCB. They also consider scenarios where the number of arms increases over time, relevant to Q&A platforms like Reddit or product review websites like Amazon. The paper provides regret upper bounds for both algorithms and discusses the sub-linearity of these bounds in relation to the distribution of means. Experiments validate the theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how to make good choices when there are many options, but some options are similar to each other. This is important in situations where you want to recommend the best product or answer to a question. The authors show that their methods work well and provide examples of how they could be used in real-life scenarios. |
