Summary of Optimal Baseline Corrections For Off-policy Contextual Bandits, by Shashank Gupta et al.
Optimal Baseline Corrections for Off-Policy Contextual Bandits
by Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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 The proposed framework unifies various control variates used in off-policy learning to reduce estimation variance. It leverages the equivalence of these approaches in different scenarios, deriving an equivalent baseline correction for each. This leads to a characterization of the variance-optimal unbiased estimator and provides a closed-form solution. The optimal estimator demonstrates improved performance and minimizes data requirements. Empirical results confirm the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new framework is developed that combines different methods used in off-policy learning to reduce uncertainty. By showing that all these methods are connected, we can create an optimal way to learn from data without bias. This leads to better results with less data needed. The new approach works well and is supported by experiments. |
