Summary of Variance-aware Linear Ucb with Deep Representation For Neural Contextual Bandits, by Ha Manh Bui et al.
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
by Ha Manh Bui, Enrique Mallada, Anqi Liu
First submitted to arxiv on: 8 Nov 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 proposed Neural-^2-LinearUCB algorithm leverages deep neural networks to balance exploration and exploitation in contextual bandits. By incorporating an upper bound of reward noise variance ^2_t, the algorithm enhances uncertainty quantification, leading to improved regret performance. The algorithm comes in two forms: oracle and practical. Theoretical analysis shows that the oracle version achieves a better regret guarantee than other neural-UCB algorithms. Empirical results demonstrate the practical method’s computational efficiency and outperformance of state-of-the-art techniques on multiple datasets, including synthetic, UCI, MNIST, and CIFAR-10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new algorithm for balancing exploration and exploitation in contextual bandits. The algorithm uses neural networks to make decisions and incorporates an estimate of the noise variance to help decide when to explore or exploit. This helps the algorithm learn faster and make better choices over time. The researchers tested their algorithm on several datasets and found that it performed well compared to other similar algorithms. |
