Summary of Efficient and Adaptive Posterior Sampling Algorithms For Bandits, by Bingshan Hu et al.
Efficient and Adaptive Posterior Sampling Algorithms for Bandits
by Bingshan Hu, Zhiming Huang, Tianyue H. Zhang, Mathias Lécuyer, Nidhi Hegde
First submitted to arxiv on: 2 May 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 We explore Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. Building upon existing work [Agrawal and Goyal, 2017], we derive a more practical regret bound that tightens the coefficient of the leading term from 288 e^{64} to 1270. Furthermore, motivated by real-world applications requiring scalability, adaptive resource allocation, and utility-computation balance, we propose two parameterized Thompson Sampling-based algorithms: TS-MA-and TS-TD-, where controls the trade-off between utility and computation. Both algorithms achieve a regret bound of O(K^{a+1}(T)/), where K is the number of arms, T is the finite learning horizon, and denotes the single round performance loss when pulling a sub-optimal arm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to make decisions in uncertain situations. They developed new algorithms that help us choose between different options when we don’t know which one will be best. The goal is to find the right balance between making good choices and using the right amount of resources. Two new methods, TS-MA-and TS-TD-, were created to achieve this balance. These algorithms can be used in real-world applications where we need to make quick decisions with limited information. |
