Summary of Sequential Harmful Shift Detection Without Labels, by Salim I. Amoukou et al.
Sequential Harmful Shift Detection Without Labels
by Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, Manuela Veloso
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 approach detects distribution shifts affecting machine learning models in continuous production environments without requiring ground truth data labels. Building upon Podkopaev and Ramdas’ [2022] work on tracking model errors with available labels, this method extends the framework to handle label-less scenarios by utilizing a proxy for true error derived from a trained error estimator’s predictions. Experimental results demonstrate high power and false alarm control under various distribution shifts, including covariate, label, geographic, and temporal changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make machine learning models better at working in real-world situations where things change over time or in different places. It does this by creating a new way to detect when the data is changing without needing exact answers (labels) for every prediction. The method works by using predictions from another trained model that estimates how wrong the original model is. This approach shows promising results in controlling false alarms and detecting changes in various situations. |
Keywords
» Artificial intelligence » Machine learning » Tracking
