54 citations · 101 across the 30 of their papers we have counts for
5 papers · 1 filter
Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
Maryam Aliakbarpour, Arnav Burudgunte, Clément Cannone +1
We extend the framework of augmented distribution testing (Aliakbarpour, Indyk, Rubinfeld, and Silwal, NeurIPS 2024) to the differentially private setting. This captures scenarios…
Optimal Algorithms for Augmented Testing of Discrete Distributions
Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld +1
We consider the problem of hypothesis testing for discrete distributions. In the standard model, where we have sample access to an underlying distribution , extensive research h…
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Y. Chen, Piotr Indyk +5
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph…
Learning-based Support Estimation in Sublinear Time
Talya Eden, Piotr Indyk, Shyam Narayanan +3
We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a ra…
Differentially Private Identity and Closeness Testing of Discrete Distributions
Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
We investigate the problems of identity and closeness testing over a discrete population from random samples. Our goal is to develop efficient testers while guaranteeing Differenti…