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Creators/Authors contains: "Watkins, Austin"

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  1. ; ; ; (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))
    We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting. 
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    Free, publicly-accessible full text available December 1, 2025
  2. ; (, Canadian Conference on Computational Geometry)
    Accepted Manuscript Full Text Available