Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

27 May 2014

Papers citing "Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds"

7 / 7 papers shown

Title
Loss Gradient Gaussian Width based Generalization and Optimization Guarantees A. Banerjee Qiaobo Li Yingxue Zhou 87 0 0 11 Jun 2024
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar MLT 41 193 0 12 Jun 2020
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data Nicolas Papernot Martín Abadi Ulfar Erlingsson Ian Goodfellow Kunal Talwar 50 1,009 0 18 Oct 2016
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy A. Beimel Kobbi Nissim Uri Stemmer 54 194 0 10 Jul 2014
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 132 573 0 08 Dec 2012
Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization Alekh Agarwal Peter L. Bartlett Pradeep Ravikumar Martin J. Wainwright 111 248 0 03 Sep 2010
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning Benjamin I. P. Rubinstein Peter L. Bartlett Ling Huang N. Taft 75 293 0 30 Nov 2009