no code implementations • NeurIPS 2021 • Jayadev Acharya, Clement Canonne, YuHan Liu, Ziteng Sun, Himanshu Tyagi
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 $\ell$ bits.
no code implementations • 26 Mar 2020 • Yihui Quek, Clement Canonne, Patrick Rebentrost
We demonstrate a quantum algorithm for noisy quantum minimum-finding that preserves the quadratic speedup of the noiseless case: our algorithm runs in time $\tilde O(\sqrt{N (1+\Delta)})$, where $\Delta$ is an upper-bound on the number of elements within the interval $\alpha$, and outputs a good approximation of the true minimum with high probability.
no code implementations • NeurIPS 2018 • Alistair Stewart, Ilias Diakonikolas, Clement Canonne
We study the general problem of testing whether an unknown discrete distribution belongs to a specified family of distributions.
no code implementations • 19 Feb 2017 • Clement Canonne, Tom Gur
More accurately, we say that a tester is $k$-(round) adaptive if it makes queries in $k+1$ rounds, where the queries in the $i$'th round may depend on the answers obtained in the previous $i-1$ rounds.
no code implementations • 9 Dec 2016 • Clement Canonne, Ilias Diakonikolas, Daniel Kane, Alistair Stewart
This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks -- the prototypical family of directed graphical models.