Summary of Optimal Classification Under Performative Distribution Shift, by Edwige Cyffers (magnet) et al.
Optimal Classification under Performative Distribution Shift
by Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif, Olivier Cappé
First submitted to arxiv on: 4 Nov 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 The proposed novel view models performative effects in algorithmic decisions as push-forward measures, generalizing existing models and enabling efficient and scalable learning strategies for distribution shifts. The framework assumes knowledge of the shift operator representing performative changes, unlike previous models requiring full data distribution specification. It can be integrated into change-of-variable-based models like VAEs or normalizing flows. Focusing on classification with a linear-in-parameters performative effect, the paper proves convexity of the performative risk under new assumptions, connecting to adversarially robust classification by reformulating the minimization of the performative risk as a min-max variational problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Performative learning is important because it helps us understand how algorithmic decisions can change the data distribution. The authors propose a new way to model these changes, called push-forward measures. This approach allows for more efficient and scalable learning strategies when dealing with changing data distributions. They also show that their method connects to adversarially robust classification, which is important for ensuring fairness in machine learning models. |
Keywords
* Artificial intelligence * Classification * Machine learning
