IAM

18thJUNE2020

READING

Shivam Garg, Vatsal Sharan, Brian Hu Zhang, Gregory Valiant. A Spectral View of Adversarially Robust Features. NeurIPS 2018.

Garg et al. propose adversarially robust features based on a graph interpretation of the training data. In this graph, training points are connected based on their distance in input space. Robust features are obtained using the eigenvectors of the Laplacian of the graph. It is theoretically shown that these features are robust, based on some assumptions on the graph. For example, the bound obtained on robustness depends on the gap between second and third eigenvalue.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: