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Creators/Authors contains: "Mao, Cheng"

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  1. ; ; (, IEEE Transactions on Information Theory)
    Free, publicly-accessible full text available February 1, 2026
  2. ; (, IEEE)
    Full Text Available 
  3. ; ; (, Proceedings of Thirty Sixth Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  4. ; ; (, Proceedings of Thirty Sixth Conference on Learning Theory)
    We consider a symmetric mixture of linear regressions with random samples from the pairwise comparison design, which can be seen as a noisy version of a type of Euclidean distance geometry problem. We analyze the expectation-maximization (EM) algorithm locally around the ground truth and establish that the sequence converges linearly, providing an $$\ell_\infty$$-norm guarantee on the estimation error of the iterates. Furthermore, we show that the limit of the EM sequence achieves the sharp rate of estimation in the $$\ell_2$$-norm, matching the information-theoretically optimal constant. We also argue through simulation that convergence from a random initialization is much more delicate in this setting, and does not appear to occur in general. Our results show that the EM algorithm can exhibit several unique behaviors when the covariate distribution is suitably structured. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; (, Chinese Journal of Polymer Science)
    Full Text Available 
  6. ; ; (, Proceedings of Thirty Sixth Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Proceedings of the annual ACM Symposium on Theory of Computing)
    Full Text Available 
  8. ; ; (, Probability Theory and Related Fields)
    Full Text Available 
  9. ; (, The Annals of Statistics)
    Full Text Available 
  10. ; ; ; (, Foundations of Computational Mathematics)
    Full Text Available