DP-XGBoost: Private Machine Learning at Scale

25 October 2021

Papers citing "DP-XGBoost: Private Machine Learning at Scale"

4 / 4 papers shown

Title
Improved Differentially Private Regression via Gradient Boosting Shuai Tang Sergul Aydore Michael Kearns Saeyoung Rho Aaron Roth Yichen Wang Yu-Xiang Wang Zhiwei Steven Wu FedML 35 4 0 06 Mar 2023
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 26 29 0 06 Oct 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 165 350 0 25 Sep 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 194 0 26 Feb 2021