Summary of One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently, by Weida Li and Yaoliang Yu
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently
by Weida Li, Yaoliang Yu
First submitted to arxiv on: 31 Oct 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 This paper explores efficient approximation techniques for probabilistic values, such as Beta Shapley values and weighted Banzhaf values, which are crucial in feature attribution and data valuation. Exact computation is often expensive, necessitating approximation methods. Prior research has shown that the choice of probabilistic value significantly impacts performance, with no universally superior option. To address this, the authors propose a one-sample-fits-all framework for approximating multiple probabilistic values simultaneously and efficiently. This framework is parameterized by a sampling vector and leverages the concept of (, )-approximation to determine the convergence rate. The authors optimize the sampling vector using this formula, achieving a one-for-all estimator with the best time complexity for all probabilistic values on average, as well as a faster generic estimator tailored to each value. The paper demonstrates the effectiveness of their approach, particularly for Beta Shapley values, including the well-known Shapley value. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to quickly and accurately calculate special kinds of values that are important in data science. These values help us understand how different features or variables contribute to an overall outcome. Right now, calculating these values can be very time-consuming and depends on the specific method used. The authors wanted to find a way to do this calculation once and then use it for many different methods. They developed a new approach that uses a special formula to optimize the calculation process. This approach is really fast and accurate, and it works well for all kinds of values, not just one specific type. |
