Clément L. Canonne

Testing Conditional Independence of Discrete Distributions

2017 · arXiv (Cornell University)

We study the problem of testing \emph{conditional independence} for discrete distributions. Specifically, given samples from a discrete random variable $(X, Y, Z)$ on domain $[\ell_1]\times[\ell_2] \times [n]$, we want to distinguish, with probability at least …

Testing Conditional Independence of Discrete Distributions

2018

We study the problem of testing conditional independence for discrete distributions. Specifically, given samples from a discrete random variable (X,Y,Z) on domain [ℓ1] × [ℓ2] × [n], we want to distinguish, with probability at least …

Sampling Correctors

2015 · arXiv (Cornell University)

In many situations, sample data is obtained from a noisy or imperfect source. In order to address such corruptions, this paper introduces the concept of a sampling corrector. Such algorithms use structure that the distribution …

Uniformity Testing Under User-Level Local Privacy

2025 · arXiv (Cornell University)

We initiate the study of distribution testing under \emph{user-level} local differential privacy, where each of $n$ users contributes $m$ samples from the unknown underlying distribution. This setting, albeit very natural, is significantly more challenging that …