Summary of A Hierarchical Decomposition For Explaining Ml Performance Discrepancies, by Jean Feng et al.
A hierarchical decomposition for explaining ML performance discrepancies
by Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann
First submitted to arxiv on: 22 Feb 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 Machine learning algorithms can exhibit varying performances across domains, making it essential to understand why these differences occur. By analyzing the performance gaps between domains, researchers can determine the most effective interventions, whether algorithmic or operational, to close these gaps. Current methods focus on aggregate decompositions of the total performance gap into two components: a shift in feature distributions and a shift in conditional outcome distributions. While this approach provides some insights, it only offers a few options for closing the performance gap. To gain a deeper understanding and suggest more targeted interventions, researchers need detailed variable-level decompositions that quantify the importance of each variable to each term in the aggregate decomposition. However, existing methods require knowledge of the full causal graph or make strong parametric assumptions. This paper introduces a nonparametric hierarchical framework that provides both aggregate and detailed decompositions for explaining performance differences across domains without requiring causal knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms can perform differently across different areas. To improve their performance, we need to understand why this happens. Researchers usually focus on overall explanations of the difference in performance, but these only provide a few ideas for making improvements. We need more detailed explanations that show how each part of the data affects the outcome. This paper introduces a new way to analyze and explain the differences in performance across different areas without needing to know the entire cause-and-effect graph or make strong assumptions about the data. |
Keywords
* Artificial intelligence * Machine learning
