Summary of How Does Variance Shape the Regret in Contextual Bandits?, by Zeyu Jia et al.
How Does Variance Shape the Regret in Contextual Bandits?
by Zeyu Jia, Jian Qian, Alexander Rakhlin, Chen-Yu Wei
First submitted to arxiv on: 16 Oct 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 paper investigates realizable contextual bandits with general function approximation, exploring how small reward variance can lead to better-than-minimax regret bounds. The authors show that the eluder dimension (d_) plays a crucial role in variance-dependent bounds, unlike minimax bounds. They consider two types of adversaries: a worst-case adversary and a stochastic adversary. The paper’s main contribution is developing a novel framework for bounding the regret in realizable contextual bandits with general function approximation, which can lead to better-than-minimax regret bounds when reward variance is small. This work has implications for improving the performance of contextual bandit algorithms in applications such as personalized medicine and recommendation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a type of machine learning problem called contextual bandits. It’s like a game where you have to make decisions based on some information, but you don’t know what the best decision is until afterwards. The authors want to see how well they can do if they make small mistakes sometimes, instead of just trying to avoid making any mistakes at all. They found that a special measure called the eluder dimension is important for figuring out how well they’ll do. This has implications for using these types of algorithms in things like personalized medicine and online recommendations. |
Keywords
* Artificial intelligence * Machine learning
