BadGD: A unified data-centric framework to identify gradient descent vulnerabilities

24 May 2024

Papers citing "BadGD: A unified data-centric framework to identify gradient descent vulnerabilities"

4 / 4 papers shown

Title
Quantifying and Mitigating Privacy Risks for Tabular Generative Models Chaoyi Zhu Jiayi Tang Hans Brouwer Juan F. Pérez Marten van Dijk Lydia Y. Chen 55 5 0 12 Mar 2024
Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation Gavin Brown Krishnamurthy Dvijotham Georgina Evans Daogao Liu Adam D. Smith Abhradeep Thakurta 34 3 0 21 Feb 2024
RQP-SGD: Differential Private Machine Learning through Noisy SGD and Randomized Quantization Ce Feng Parv Venkitasubramaniam 19 1 0 09 Feb 2024
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees Anastasia Koloskova Hadrien Hendrikx Sebastian U. Stich 99 48 0 02 May 2023