Summary of Ucb Exploration For Fixed-budget Bayesian Best Arm Identification, by Rong J.b. Zhu and Yanqi Qiu
UCB Exploration for Fixed-Budget Bayesian Best Arm Identification
by Rong J.B. Zhu, Yanqi Qiu
First submitted to arxiv on: 9 Aug 2024
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 The paper proposes an upper confidence bound (UCB) exploration algorithm for best-arm identification in the fixed-budget setting under a Bayesian framework. The algorithm aims to learn prior information and enhance the performance of UCB-based algorithms, which have been shown to work well in this problem domain. The proposed approach achieves theoretical and empirical efficiency, with bounds on failure probability and simple regret established. Empirical results demonstrate consistent outperformance of state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies a way to find the best option among multiple choices (called “arms”) when there’s a limited number of tries. It proposes a new algorithm that uses information about what we already know about each arm to make better choices. The algorithm is designed for situations where we can’t try all the arms, but we want to be sure we’re choosing the best one. The results show that this algorithm works well and does better than other approaches. |
Keywords
* Artificial intelligence * Probability
