Summary of Fairness Hub Technical Briefs: Definition and Detection Of Distribution Shift, by Nicolas Acevedo et al.
Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift
by Nicolas Acevedo, Carmen Cortez, Chris Brooks, Rene Kizilcec, Renzhe Yu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 paper investigates distribution shift issues in educational settings, focusing on standard prediction problems. Distribution shift refers to the mismatch between training data and real-world application data, which can lead to performance reductions due to factors such as sampling issues, environmental changes, or emergence of new scenarios. The authors define and detect distribution shifts using machine learning models, with applications in time-series forecasting and large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how education settings are affected by a common problem called distribution shift. In simple terms, it’s when the data used to train a model doesn’t match what happens in real life. This can happen for many reasons like changes in rules or policies, new situations emerging, or even just the way data is collected. The paper tries to understand and detect these shifts using machine learning techniques. |
Keywords
» Artificial intelligence » Machine learning » Time series
