On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach

3 April 2023

Papers citing "On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach"

6 / 6 papers shown

Title
The Deep Kernelized Autoencoder Michael C. Kampffmeyer Sigurd Løkse F. Bianchi Robert Jenssen L. Livi 36 18 0 19 Jul 2018
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Borja Balle Yu Wang MLT 66 403 0 16 May 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 57 418 0 05 Feb 2018
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi Luigi Carratino Lorenzo Rosasco 63 196 0 31 May 2017
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 191 6,109 0 01 Jul 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 228 885 0 06 Nov 2015