Summary of Scalable Thompson Sampling Via Ensemble++ Agent, by Yingru Li et al.
Scalable Thompson Sampling via Ensemble++ Agent
by Yingru Li, Jiawei Xu, Baoxiang Wang, Zhi-Quan Luo
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Theory (cs.IT); 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 In this paper, researchers develop an improved version of Thompson Sampling, a widely used method for balancing exploration and exploitation in decision-making processes. The original method has limitations in large-scale or non-conjugate settings, making it difficult to adopt in real-world scenarios. To address these limitations, the authors propose Ensemble++, a scalable agent that uses a shared-factor ensemble update architecture and a random linear combination scheme. This approach allows for efficient computation and reduced overhead, while maintaining comparable regret guarantees to exact Thompson Sampling. The authors also introduce a neural extension that enables handling nonlinear rewards and complex environments. Experimental results demonstrate the superiority of Ensemble++ in terms of sample efficiency and computational scalability across various environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions by improving how we balance trying new things and sticking with what works. It’s like trying to find the best route when you’re lost – sometimes you need to explore and try new roads, but other times it’s safer to stick with a familiar path. The researchers develop a new way of doing this called Ensemble++, which is faster and more efficient than before. This means we can make decisions quicker and better, even in complex situations. They also show that this works well for things like recommending music or ads online. |
