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Scalable and Provably Accurate Algorithms for Differentially Private
  Distributed Decision Tree Learning
v1v2v3 (latest)

Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning

19 December 2020
Kai Wang
Travis Dick
Maria-Florina Balcan
    FedML
ArXiv (abs)PDFHTML

Papers citing "Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning"

5 / 5 papers shown
Title
FedGA-Tree: Federated Decision Tree using Genetic Algorithm
Anh Van Nguyen
Diego Klabjan
FedML
12
0
0
09 Jun 2025
How to DP-fy ML: A Practical Guide to Machine Learning with Differential
  Privacy
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
156
183
0
01 Mar 2023
Private Boosted Decision Trees via Smooth Re-Weighting
Private Boosted Decision Trees via Smooth Re-Weighting
Vahid R. Asadi
M. Carmosino
Mohammad Jahanara
Akbar Rafiey
Bahar Salamatian
82
2
0
29 Jan 2022
Accuracy, Interpretability, and Differential Privacy via Explainable
  Boosting
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori
R. Caruana
Zhiqi Bu
J. Shen
Janardhan Kulkarni
83
38
0
17 Jun 2021
SoK: Privacy-Preserving Collaborative Tree-based Model Learning
SoK: Privacy-Preserving Collaborative Tree-based Model Learning
Sylvain Chatel
Apostolos Pyrgelis
J. Troncoso-Pastoriza
Jean-Pierre Hubaux
73
15
0
16 Mar 2021
1