Summary of Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits, by Ziyi Huang et al.
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
by Ziyi Huang, Henry Lam, Haofeng Zhang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers investigate the theoretical justification for Bayesian bandit algorithms that use approximate Bayesian inference. Specifically, they focus on contextual bandit problems and analyze the impact of approximate inference on two popular algorithms: Linear Thompson Sampling (LinTS) and Linear Bayesian Upper Confidence Bound (LinBUCB). The authors demonstrate that these algorithms can still achieve their original rates of regret upper bound when used with approximate inference, but with a larger constant term. Additionally, they show that LinBUCB can improve the regret rate compared to LinTS, matching the minimax optimal rate in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to use Bayesian bandit algorithms in real-world situations where there’s not enough data to make perfect predictions. The researchers compare two different methods: Linear Thompson Sampling (LinTS) and Linear Bayesian Upper Confidence Bound (LinBUCB). They show that both methods can work well even when they’re not perfect, but LinBUCB might be a better choice in some situations. |
Keywords
» Artificial intelligence » Bayesian inference » Inference
