Summary of Leveraging (biased) Information: Multi-armed Bandits with Offline Data, by Wang Chi Cheung et al.
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data
by Wang Chi Cheung, Lixing Lyu
First submitted to arxiv on: 4 May 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 A machine learning-based approach is proposed for facilitating online learning in stochastic multi-armed bandits by leveraging offline data. The method, called MIN-UCB, adaptively chooses to utilize offline data when it’s informative and ignores it otherwise. MIN-UCB outperforms the UCB policy when a non-trivial upper bound on the difference between the probability distributions is given. This approach is shown to be tight in terms of both instance-independent and dependent regret bounds. Numerical experiments corroborate the theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to use data collected offline, like from old sensors or databases, to improve online learning in complex situations called multi-armed bandits. This is useful because it can help make decisions better when there’s uncertainty and limited information. They created a new strategy called MIN-UCB that decides when to use the offline data and when to ignore it. The results show that this approach works well and can even outperform other methods in some cases. |
Keywords
» Artificial intelligence » Machine learning » Online learning » Probability
