Summary of The Limitations Of Model Retraining in the Face Of Performativity, by Anmol Kabra et al.
The Limitations of Model Retraining in the Face of Performativity
by Anmol Kabra, Kumar Kshitij Patel
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 investigates stochastic optimization in the context of performative shifts, where data distributions change due to deployed models. The authors demonstrate that naive retraining can be suboptimal even for simple distribution shifts, and this issue worsens when retraining is done with a finite number of samples. To address this, the researchers show that adding regularization during retraining corrects these issues, achieving provably optimal models in the presence of performative effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper looks at how machine learning models behave when the data they’re trained on changes because of the model itself. The authors found that simply retraining a model to adapt to these changes can sometimes make things worse, not better. To fix this, they recommend adding some extra rules during retraining to ensure the model is as good as it can be in this changing environment. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regularization
