Summary of Robust Offline Policy Learning with Observational Data From Multiple Sources, by Aldo Gael Carranza et al.
Robust Offline Policy Learning with Observational Data from Multiple Sources
by Aldo Gael Carranza, Susan Athey
First submitted to arxiv on: 11 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 proposed minimax regret optimization objective aims to learn a personalized decision policy that generalizes well across diverse target settings by utilizing observational bandit feedback data from multiple heterogeneous data sources. A tailored policy learning algorithm is developed, combining doubly robust offline policy evaluation techniques and no-regret learning algorithms for minimax optimization. The approach achieves the minimal worst-case mixture regret up to a moderated vanishing rate of the total data across all sources. Experimental results demonstrate the benefits of this approach for learning robust decision policies from multiple data sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn how to make good decisions by using information from many different places. It develops a new way to do this that works well even when we don’t know what’s going on in each place. This is important because it can help us make better choices in situations where things are uncertain or changing. |
Keywords
* Artificial intelligence * Optimization
