Summary of Identifying the Best Arm in the Presence Of Global Environment Shifts, by Phurinut Srisawad et al.
Identifying the Best Arm in the Presence of Global Environment Shifts
by Phurinut Srisawad, Juergen Branke, Long Tran-Thanh
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the Best-Arm Identification problem in a non-stationary stochastic bandits setting, where environmental changes affect all arms. The goal is to identify the best arm given a fixed budget while accounting for these changes. Unlike previous solutions, this novel approach utilizes global environmental shifts to improve performance. A selection policy and allocation policy, LinLUCB, are developed to address this issue. Experimental results show significant improvements over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving a problem in machine learning called Best-Arm Identification. Imagine you have many options, like different medicine treatments, and you want to find the best one without trying all of them. This gets harder when the environment changes, like if a new virus emerges. The current solutions don’t work well when this happens. To fix this, researchers created new methods that take into account these environmental shifts. They tested their ideas and found they were much better than what’s currently available. |
Keywords
» Artificial intelligence » Machine learning
