Summary of Haver: Instance-dependent Error Bounds For Maximum Mean Estimation and Applications to Q-learning, by Tuan Ngo Nguyen and Kwang-sung Jun
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning
by Tuan Ngo Nguyen, Kwang-Sung Jun
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 We study a machine learning problem called estimating the value of the largest mean among K distributions using samples from them. This arises in tasks like Q-learning and Monte Carlo tree search. While existing algorithms have performance analyses, they’re limited to bias rather than precise error metrics. We propose HAVER (Head Averaging) and analyze its mean squared error, revealing two compelling aspects: first, HAVER estimates the maximum mean as well as an oracle that knows the best distribution’s identity; second, HAVER exhibits better rates than this oracle when many distributions are near the best one. These improvements are novel in the literature, and we prove a naive algorithm doesn’t achieve these bounds. We confirm our findings via numerical experiments on bandits and Q-learning scenarios, where HAVER outperforms baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky machine learning problem called “estimating the best mean.” Imagine you have many options, like different video games or restaurants, and you want to find the one that’s the most enjoyable. The researchers propose a new way of doing this, called HAVER, which is better than other methods at finding the best option. They also show that their method works well when there are many good options that are close in quality. This could be useful in games like poker or video games, where you need to decide which action to take next. |
Keywords
* Artificial intelligence * Machine learning
