1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Efficient and Scalable Implementation of Differentially Private Deep Learning without Shortcuts
Sebastian Rodriguez Beltran, Marlon Tobaben, Joonas Jälkö +2
Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for training machine learning models under differential privacy (DP). The most common DP-SGD p…
cs.LG2021★ 1 cited
D3p -- A Python Package for Differentially-Private Probabilistic Programming
Lukas Prediger, Niki Loppi, Samuel Kaski +1
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general appl…