• Corpus ID: 247518540

Understanding Intrinsic Robustness Using Label Uncertainty

@inproceedings{Zhang2021UnderstandingIR,
  title={Understanding Intrinsic Robustness Using Label Uncertainty},
  author={Xiao Zhang and David Evans},
  year={2021}
}
A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studying the concentration of measure, but we argue standard concentration fails to fully characterize the intrinsic robustness of a classification problem since it ignores data labels which are essential to any classification task. Building on a novel definition of label uncertainty, we empirically demonstrate that error… 
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