Differentially Private Fr\'echet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
Differential privacy has become crucial in the real-world deployment of statistical and
machine learning algorithms with rigorous privacy guarantees. The earliest statistical …
machine learning algorithms with rigorous privacy guarantees. The earliest statistical …
Improved differentially private Riemannian optimization: Fast sampling and variance reduction
A common step in differentially private ({DP}) Riemannian optimization is sampling from the
(tangent) Gaussian distribution as noise needs to be generated in the tangent space to …
(tangent) Gaussian distribution as noise needs to be generated in the tangent space to …
Changes from classical statistics to modern statistics and data science
K Zhang, S Liu, M Xiong - arXiv preprint arXiv:2211.03756, 2022 - arxiv.org
A coordinate system is a foundation for every quantitative science, engineering, and
medicine. Classical physics and statistics are based on the Cartesian coordinate system …
medicine. Classical physics and statistics are based on the Cartesian coordinate system …
Connecting silos with distributed and private computation
P Vepakomma - 2024 - dspace.mit.edu
Data in today's world is increasingly siloed across a wide variety of entities with varying
resource constraints. The quality of wisdom generated from a collaborative processing of …
resource constraints. The quality of wisdom generated from a collaborative processing of …