Summary of Distributionally Robust Policy Learning Under Concept Drifts, by Jingyuan Wang et al.
Distributionally Robust Policy Learning under Concept Drifts
by Jingyuan Wang, Zhimei Ren, Ruohan Zhan, Zhengyuan Zhou
First submitted to arxiv on: 18 Dec 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 This paper focuses on robust policy learning under concept drift, where only the conditional relationship between the outcome and covariate changes. Traditional joint-modeling strategies can be overly conservative when considering distributional shifts. The authors propose a doubly-robust estimator to evaluate the worst-case average reward of a given policy under perturbed conditional distributions. This estimator enjoys asymptotic normality even with slower-than-root-n rate estimation of nuisance parameters. A learning algorithm is developed to maximize the estimated policy value within a given policy class, and its sub-optimality gap is shown to be O(κ(Π)n^(-1/2)), where κ(Π) is the entropy integral under the Hamming distance. The proposed methods are evaluated in numerical studies, demonstrating significant improvements over existing benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making better decisions even when things change unexpectedly. Usually, we try to prepare for all possible changes, but that can be too cautious. Instead, this research focuses on adapting to changes in the relationship between what we observe (like weather) and what we want to happen (like a good day). The authors create new methods to find the best decision when things change like this. They test these methods and show they work much better than previous approaches. |
