Summary of On the Geometry Of Regularization in Adversarial Training: High-dimensional Asymptotics and Generalization Bounds, by Matteo Vilucchio et al.
On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds
by Matteo Vilucchio, Nikolaos Tsilivis, Bruno Loureiro, Julia Kempe
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 In this paper, researchers investigate how to choose the right type of regularization in high-dimensional adversarial training for binary classification. They focus on understanding how different types of attacks and regularization norms affect the performance of a robust, regularized empirical risk minimizer. The authors derive an exact asymptotic description of the optimal choice of regularization norm based on various attack types and provide bounds on the Rademacher Complexity to ensure uniform convergence. Their findings confirm that as perturbations grow in size, the type of regularization becomes increasingly important for adversarial training, especially when data is scarce. This work has implications for developing robust machine learning models that can perform well even when faced with noisy or contaminated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make machine learning models more robust against bad data. When we’re working with limited information, it’s important to control the complexity of our model so it doesn’t overfit. The researchers explore different ways to do this and find that as the amount of noise in the data grows, choosing the right type of regularization becomes even more crucial. This is important because it helps us build models that can handle real-world challenges like noisy or contaminated data. |
Keywords
» Artificial intelligence » Classification » Machine learning » Regularization
