Summary of Distributionally Robust Policy Evaluation Under General Covariate Shift in Contextual Bandits, by Yihong Guo et al.
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits
by Yihong Guo, Hao Liu, Yisong Yue, Anqi Liu
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed method enhances offline policy evaluation in contextual bandits by developing a distributionally robust approach to handle covariate shifts. It applies robust regression to estimate the conditional reward distribution from logging data, which is then integrated into established evaluation frameworks (direct and doubly robust methods). Theoretical analysis shows that the proposed estimators provide a finite sample upper bound for bias, making them more reliable than traditional methods. Empirical results demonstrate significant performance improvements in 90% of cases under policy shift-only settings and 72% of scenarios under general covariate shift settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make sure that offline policy evaluation is accurate when the context or policy changes. It’s like trying to predict how well a car will perform based on past data, but what if the weather or road conditions change? The method uses a special type of regression that can handle these kinds of shifts. The results show that this approach is much better than traditional methods in many cases. |
Keywords
* Artificial intelligence * Regression
