558 citations · 3.6k across the 65 of their papers we have counts for
5 papers · 1 filter
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika +5
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, e…
Generalized Resilience and Robust Statistics
Banghua Zhu, Jiantao Jiao, Jacob Steinhardt
Robust statistics traditionally focuses on outliers, or perturbations in total variation distance. However, a dataset could be corrupted in many other ways, such as systematic meas…
Testing Robustness Against Unforeseen Adversaries
Max Kaufmann, Daniel Kang, Yi Sun +9
Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-wor…
Natural Adversarial Examples
Dan Hendrycks, Kevin Zhao, Steven Basart +2
We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtrati…
Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang, Yi Sun, Tom Brown +2
We study the transfer of adversarial robustness of deep neural networks between different perturbation types. While most work on adversarial examples has focused on and…