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Creators/Authors contains: "Canonne, Clement"

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  1. ; ; ; (, 38th Annual Conference on Learning Theory (COLT 2025))
    We extend the framework of augmented distribution testing (Aliakbarpour, Indyk, Rubinfeld, and Silwal, NeurIPS 2024) to the differentially private setting. This captures scenarios where a data ana- lyst must perform hypothesis testing tasks on sensitive data, but is able to leverage prior knowledge (public, but possibly erroneous or untrusted) about the data distribution. We design private algorithms in this augmented setting for three flagship distribution testing tasks, uniformity, identity, and closeness testing, whose sample complexity smoothly scales with the claimed quality of the auxiliary information. We complement our algorithms with information- theoretic lower bounds, showing that their sample complexity is optimal (up to logarithmic factors). Keywords: distribution testing, identity testing, closeness testing, differential privacy, learning- augmented algorithms 
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    Free, publicly-accessible full text available June 30, 2026
  2. ; ; ; (, Theory and Practice of Differential Privacy (TPDP 2025))
    We extend the framework of augmented distribution testing (Aliakbarpour, Indyk, Rubinfeld, and Silwal, NeurIPS 2024) to the differentially private setting. This captures scenarios where a data analyst must perform hypothesis testing tasks on sensitive data, but is able to leverage prior knowledge (public, but possibly erroneous or untrusted) about the data distribution. We design private algorithms in this augmented setting for three flagship distribution testing tasks, uniformity, identity, and closeness testing, whose sample complexity smoothly scales with the claimed quality of the auxiliary information. We complement our algorithms with information-theoretic lower bounds, showing that their sample complexity is optimal (up to logarithmic factors). 
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    Free, publicly-accessible full text available June 2, 2026
  3. ; ; ; (, Proceedings of Machine Learning Research)
    Loh, Po-Ling; Raginsky, Maxim (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, IEEE Transactions on Information Theory)
    Full Text Available 
  5. ; ; ; ; (, Advances in neural information processing systems)
    We obtain tight minimax rates for the problem of distributed estimation of discrete distributions under communication constraints, where n users observing m samples each can broadcast only ℓ bits. Our main result is a tight characterization (up to logarithmic factors) of the error rate as a function of m, ℓ, the domain size, and the number of users under most regimes of interest. 
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    Accepted Manuscript Full Text Available
  6. ; ; (, IEEE Transactions on Information Theory)
    null (Ed.)
    Full Text Available 
  7. ; ; (, IEEE Transactions on Information Theory)
    null (Ed.)
    Full Text Available 
  8. ; ; ; ; (, IEEE Journal on Selected Areas in Information Theory)
    Full Text Available 
  9. ; ; ; (, Advances in neural information processing systems)
    Ranzato, M; Beygelzimer, A; Dauphin, Y; Liang, P. S.; Wortman Vaughan, J (Ed.)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, Proceedings of the 32th Annual ACM-SIAM Symposium on Discrete Algorithms)
    null (Ed.)
    Full Text Available