no code implementations • 19 Oct 2023 • Syomantak Chaudhuri, Konstantin Miagkov, Thomas A. Courtade
As a consequence, users with less but differing privacy requirements are all given more privacy than they require, in equal amounts.
no code implementations • 27 Apr 2023 • Syomantak Chaudhuri, Thomas A. Courtade
Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm.
no code implementations • 19 Jul 2017 • Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade
We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice.
no code implementations • 24 Apr 2017 • Ashwin Pananjady, Martin J. Wainwright, Thomas A. Courtade
The multivariate linear regression model with shuffled data and additive Gaussian noise arises in various correspondence estimation and matching problems.
no code implementations • 9 Aug 2016 • Ashwin Pananjady, Martin J. Wainwright, Thomas A. Courtade
Consider a noisy linear observation model with an unknown permutation, based on observing $y = \Pi^* A x^* + w$, where $x^* \in \mathbb{R}^d$ is an unknown vector, $\Pi^*$ is an unknown $n \times n$ permutation matrix, and $w \in \mathbb{R}^n$ is additive Gaussian noise.