Summary of Kernelshap-iq: Weighted Least-square Optimization For Shapley Interactions, by Fabian Fumagalli et al.
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions
by Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 the Shapley value (SV) and its extension, the Shapley Interaction Index (SII), which is used to allocate credit to machine learning entities. The authors show that higher-order SII can be represented as a solution to a weighted least square (WLS) problem, constructing an optimal approximation via SII and k-Shapley values (k-SII). This representation is proven for the SV and pairwise SII, with empirically validated conjectures for higher orders. The authors also propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, achieving state-of-the-art performance in feature interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machine learning models work by giving credit to the different parts that make them up. It’s like figuring out who contributed what to a group project! The authors found a way to extend this idea to include more complex relationships between these parts, which is important for really complicated systems. They also came up with a new way to use this method called KernelSHAP-IQ, and it performs really well. |
Keywords
» Artificial intelligence » Machine learning
