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Creators/Authors contains: "Hsieh, C J"

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  1. ; ; ; ; ; (, International Conference on Tools and Algorithms for the Construction and Analysis of Systems)
    Free, publicly-accessible full text available May 5, 2026
  2. ; ; ; ; ; (, Forty-first International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; (, ICML)
    Accepted Manuscript Full Text Available
  4. ; ; (, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, Journal of machine learning research)
    We present a probabilistic framework for studying adversarial attacks on discrete data. Based on this framework, we derive a perturbation-based method, Greedy Attack, and a scalable learning-based method, Gumbel Attack, that illustrate various tradeoffs in the design of attacks. We demonstrate the effectiveness of these methods using both quantitative metrics and human evaluation on various stateof-the-art models for text classification, including a word-based CNN, a character-based CNN and an LSTM. As an example of our results, we show that the accuracy of character-based convolutional networks drops to the level of random selection by modifying only five characters through Greedy Attack. 
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    Accepted Manuscript Full Text Available