Summary of Concept-driven Off Policy Evaluation, by Ritam Majumdar et al.
Concept-driven Off Policy Evaluation
by Ritam Majumdar, Jack Teversham, Sonali Parbhoo
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 work addresses the challenges in off-policy decision evaluation by incorporating human-explainable concepts into Off-Policy Evaluation (OPE). By leveraging Concept Bottleneck Models (CBMs), the researchers demonstrate that using predefined concepts can reduce variance and improve predictions. A family of concept-based OPE estimators is introduced, which are shown to remain unbiased and decrease variance when concepts are known. To handle real-world applications lacking predefined concepts, an end-to-end algorithm is developed to learn interpretable, concise, and diverse parameterized concepts optimized for variance reduction. Experimental results on synthetic and real-world datasets demonstrate significant improvements in OPE performance using both known and learned concept-based estimators. Moreover, the approach allows for targeted interventions on specific concepts, enhancing the quality of these estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tackles a problem in machine learning where it’s hard to make good decisions when you don’t have enough data. They use an idea called Concept Bottleneck Models to help with this problem. This means using simple ideas or concepts that explain why something happens. The researchers show that by using these concepts, they can make better predictions and understand things better. They also developed a way to learn these concepts from the data itself, which is useful when we don’t have any predefined concepts. The results of their experiments are promising, showing that this approach works well for both synthetic and real-world datasets. |
Keywords
» Artificial intelligence » Machine learning
