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Creators/Authors contains: "Liu, Xiyang"

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  1. ; ; ; ; (, In 37th Conference on Neural Information Processing Systems (NeurIPS). Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 10, 2025
  2. ; ; ; ; ; ; ; (, Conference on Learning Theory. The 37th Annual Conference on Learning Theory (COLT 2024))
    We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of X⊤X, where X is the design matrix. All prior private algorithms for this task require either d3/2 examples, error growing polynomially with the condition number, or exponential time. Our near-optimal accuracy guarantee holds for any dataset with bounded statistical leverage and bounded residuals. Technically, we build on the approach of Brown et al. (2023) for private mean estimation, adding scaled noise to a carefully designed stable nonprivate estimator of the empirical regression vector. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; (, 37th Annual Conference on Learning Theory (COLT 2024))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; (, Conference on Learning Theory)
  5. ; ; ; ; (, In 37th Conference on Neural Information Processing Systems (NeurIPS). Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; (, IEEE Journal on Selected Areas in Information Theory)
    Full Text Available 
  7. ; ; ; ; ; (, 2021 IEEE International Symposium on Information Theory (ISIT))
    null (Ed.)
    Full Text Available 
  8. ; ; ; ; ; (, Proceedings of the 38th International Conference on Machine Learning,)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available