Summary of Contextual Bandits For Unbounded Context Distributions, by Puning Zhao et al.
Contextual Bandits for Unbounded Context Distributions
by Puning Zhao, Jiafei Wu, Zhe Liu, Huiwen Wu
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper introduces a solution to the nonparametric contextual bandit problem with unbounded contexts, achieving both exploration-exploitation and bias-variance tradeoffs. It proposes two nearest neighbor methods combined with UCB exploration: one using a fixed k and another using adaptive k. The first method achieves minimax optimal regret under certain margin conditions and light-tailed context distributions. The second method achieves an expected regret of O(T(1-(α+1)β/((α+(d+2)β)))+T(-β)), which matches the minimax lower bound up to logarithmic factors, indicating approximate optimality. This work contributes to the development of contextual bandit models for sequential decision-making problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a long-standing problem in machine learning: nonparametric contextual bandits with unbounded contexts. It introduces two new methods that balance exploration and exploitation while controlling bias and variance. The first method uses a fixed number of nearest neighbors, while the second adapts to changing conditions. This work is important because it helps us make better decisions when we don’t know what will happen next. |
Keywords
» Artificial intelligence » Machine learning » Nearest neighbor
