Summary of Improving Thompson Sampling Via Information Relaxation For Budgeted Multi-armed Bandits, by Woojin Jeong et al.
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits
by Woojin Jeong, Seungki Min
First submitted to arxiv on: 28 Aug 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 Bayesian budgeted multi-armed bandit problem is addressed in this research paper, where each arm consumes different resources and there’s a budget constraint. The proposed algorithm, Budgeted Thompson Sampling (BTS), offers a heuristic but doesn’t consider remaining budget information. To address this, the authors adopt the Information Relaxation Sampling framework, proposing algorithms that optimize decisions with respect to the budget constraint. These algorithms are benchmarked against conventional benchmarks, showing incremental improvements in various settings, including a real-world example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem where you have many options (arms) and limited resources. Each option uses different amounts of resources when chosen. The authors developed an algorithm called Budgeted Thompson Sampling that helps make good choices considering the remaining budget. They also created new benchmarks to measure how well this algorithm performs compared to others. |
