Summary of Suremap: Simultaneous Mean Estimation For Single-task and Multi-task Disaggregated Evaluation, by Mikhail Khodak et al.
SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation
by Mikhail Khodak, Lester Mackey, Alexandra Chouldechova, Miroslav Dudík
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); 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 paper proposes a novel method called SureMap for conducting disaggregated evaluations of machine learning models, specifically designed to tackle the multi-task problem where multiple clients seek to evaluate a single model in their own data setting. This approach transforms the problem into structured simultaneous Gaussian mean estimation and incorporates external data to improve efficiency. The authors demonstrate significant accuracy improvements over several strong competitors on various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Disaggregated evaluation is important for assessing how well AI systems perform on different groups of people. However, it can be challenging because the data might be limited, and the subgroups might be small. This paper proposes a new way to evaluate machine learning models called SureMap. It’s designed for situations where multiple companies or organizations want to use the same model but need to know how well it will work on their specific group of people. The method is efficient because it uses external data, such as information from the model creator or other users, and doesn’t require cross-validation. |
Keywords
» Artificial intelligence » Machine learning » Multi task
