A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

24 July 2023

Papers citing "A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning"

10 / 10 papers shown

Title
Differentially Private Estimation of Heterogeneous Causal Effects Fengshi Niu Harsha Nori B. Quistorff R. Caruana Donald Ngwe A. Kannan CML 69 14 0 22 Feb 2022
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 82 389 0 04 Jul 2018
Tight Lower Bounds for Differentially Private Selection Thomas Steinke Jonathan R. Ullman 77 75 0 10 Apr 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 255 4,135 0 18 Oct 2016
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach John C. Duchi Peter Glynn Hongseok Namkoong 104 322 0 11 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 207 6,130 0 01 Jul 2016
Recommendations as Treatments: Debiasing Learning and Evaluation Tobias Schnabel Adith Swaminathan Ashudeep Singh Navin Chandak Thorsten Joachims CML 162 687 0 17 Feb 2016
Near-Optimal Algorithms for Differentially-Private Principal Components Kamalika Chaudhuri Anand D. Sarwate Kaushik Sinha 82 157 0 12 Jul 2012
Performance guarantees for individualized treatment rules Min Qian Susan Murphy 303 558 0 17 May 2011
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning Benjamin I. P. Rubinstein Peter L. Bartlett Ling Huang N. Taft 104 295 0 30 Nov 2009