Summary of Distributed Thompson Sampling Under Constrained Communication, by Saba Zerefa et al.
Distributed Thompson sampling under constrained communication
by Saba Zerefa, Zhaolin Ren, Haitong Ma, Na Li
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Optimization and Control (math.OC); 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 This paper presents a novel approach to Bayesian optimization in multi-agent scenarios. The authors employ distributed Thompson sampling, leveraging Gaussian processes as surrogate models to optimize black-box functions. Each agent receives sampled points from neighbors, and utilizes its own Gaussian process to model the objective function. Theoretical bounds are established on Bayesian average regret and simple regret, dependent on the communication graph’s structure. Unlike batch Bayesian optimization, these bounds apply even in constrained communication scenarios. Numerical simulations demonstrate the efficacy of the algorithm, highlighting the importance of graph connectivity for improved regret convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores a new way to optimize functions in complex systems where many agents are involved. The authors use a method called Thompson sampling, which helps find the best solution by averaging guesses from multiple “experts”. Each expert gets information from its neighbors and uses this to update its own understanding of the function being optimized. The authors prove that their approach works well even when there’s limited communication between experts. They tested it on some classic optimization problems and showed that it can help find better solutions faster. |
Keywords
» Artificial intelligence » Objective function » Optimization
