Summary of Stability Evaluation Via Distributional Perturbation Analysis, by Jose Blanchet et al.
Stability Evaluation via Distributional Perturbation Analysis
by Jose Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 aims to develop a novel stability evaluation criterion for learning models, which can ensure reliable deployment in out-of-sample environments. This is achieved by defining a minimal perturbation required on the observed dataset to induce a prescribed deterioration in risk evaluation. The optimal transport (OT) discrepancy with moment constraints on the (sample, density) space is used to quantify this perturbation. The proposed criterion addresses both data corruptions and sub-population shifts, which are common types of distribution shifts in real-world scenarios. To make it more practical, the paper presents tractable convex formulations and computational methods tailored to different classes of loss functions. Empirically, the study validates the utility of the stability evaluation criterion across various real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to test how well machine learning models will work in situations they haven’t seen before. This is important because models often don’t perform as well when deployed in new environments. The team created a method that measures how much change is needed on the data for the model’s performance to drop by a certain amount. They used this method to evaluate different types of models and features, showing that it can help compare their stability and provide guidance on how to improve them. |
Keywords
» Artificial intelligence » Machine learning
