Summary of Order-optimal Regret in Distributed Kernel Bandits Using Uniform Sampling with Shared Randomness, by Nikola Pavlovic et al.
Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomness
by Nikola Pavlovic, Sudeep Salgia, Qing Zhao
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); 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 explore distributed kernel bandits, where multiple agents collaborate to optimize an unknown reward function. Each agent queries the function and shares information with a central server to minimize regret over time. The proposed algorithm achieves optimal regret order with sublinear communication costs by using uniform exploration at local agents and shared randomness with the central server. This approach allows for a learning rate similar to centralized settings while maintaining a diminishing rate of communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can work together to solve a big problem. Imagine you have many computers, each trying to figure out what’s best to do next. They need to share information with each other to get the right answer. The researchers came up with a new way for these computers to work together that makes it more efficient and effective. |
