Summary of Towards Domain Adaptive Neural Contextual Bandits, by Ziyan Wang et al.
Towards Domain Adaptive Neural Contextual Bandits
by Ziyan Wang, Xiaoming Huo, Hao Wang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel general domain adaptation method for contextual bandits is introduced, enabling effective decision-making in diverse settings with distribution shifts. The proposed approach leverages feedback from a source domain to adapt a bandit model for a target domain, achieving sub-linear regret bounds. Empirical results demonstrate superior performance on real-world datasets compared to state-of-the-art algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to help computers make better decisions when the situation changes. Right now, it’s hard to use what we learn in one place (like studying mice) to make good choices in another place (like studying humans). This paper creates a special method that lets us take what we learned from one source and apply it to another with similar problems. It works well and does better than other methods on real-world tasks. |
Keywords
» Artificial intelligence » Domain adaptation
