Summary of Assessing Robustness Of Machine Learning Models Using Covariate Perturbations, by Arun Prakash R et al.
Assessing Robustness of Machine Learning Models using Covariate Perturbations
by Arun Prakash R, Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
First submitted to arxiv on: 2 Aug 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 framework assesses the robustness of machine learning models against adversarial attacks and changes in input data through covariate perturbation techniques. The comprehensive approach includes various perturbation strategies for numeric and non-numeric variables, as well as summaries to compare model robustness across scenarios. Local robustness diagnosis identifies unstable regions in the data, enhancing overall model robustness. Empirical studies on real-world datasets demonstrate the framework’s effectiveness in comparing robustness across models, identifying instabilities, and improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are being used more and more to make important decisions. But what if someone tries to trick the model or change the data it’s using? We need to make sure these models can handle unexpected changes. This paper shows how to test machine learning models for their ability to withstand attacks and changes in data. It uses special techniques called covariate perturbations to see how well a model performs when things are changed slightly. The results show that this approach is really good at comparing the robustness of different models, finding where they might be weak, and making them better overall. |
Keywords
» Artificial intelligence » Machine learning
