Summary of Second Order Bounds For Contextual Bandits with Function Approximation, by Aldo Pacchiano
Second Order Bounds for Contextual Bandits with Function Approximation
by Aldo Pacchiano
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes new algorithms for contextual bandits with function approximation, addressing a long-standing problem in machine learning. The optimistic least squares algorithm has been widely used, but it suffers from regret scaling with the square root of time horizon, which can be problematic when measurement noise is changing and small. The authors introduce novel methods that achieve regret bounds scaling with the square root of the sum of measurement variances, a significant improvement. This breakthrough generalizes techniques for contextual linear problems and has far-reaching implications for applications such as personalized advertising and recommendation systems. The proposed algorithms outperform existing methods in terms of regret, offering a more robust approach to contextual bandits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning called contextual bandits with function approximation. Right now, the best algorithm is the optimistic least squares algorithm, but it has a flaw – its performance gets worse as time goes on. The authors came up with new ways to do this that are much better. Instead of getting worse over time, their algorithms get better in proportion to how well they can handle changing noise in the data. This is important because it means we can use these algorithms in real-world situations where things change a lot. |
Keywords
* Artificial intelligence * Machine learning
