Differential Privacy Over Riemannian Manifolds
Authors: Matthew Reimherr, Karthik Bharath, Carlos Soto
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we numerically explore two examples that are common in statistics. In the first and second panels of Figure 1 we show simulation results which illustrate Theorems 2 and 3 and compare the utility of the Euclidean counterpart. Simulations are done in Matlab on a desktop computer with an Intel Xeon processor at 3.60GHz with 31.9 GB of RAM running Windows 10. |
| Researcher Affiliation | Academia | Matthew Reimherr Department of Statistics Pennsylvania State University University Park, PA mreimherr@psu.edu Karthik Bharath School of Mathematical Sciences University of Nottingham Nottingham, UK Karthik.Bharath@nottingham.ac.uk Carlos Soto Department of Statistics Pennsylvania State University University Park, PA cjs7363@psu.edu |
| Pseudocode | No | The paper describes algorithms but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | All code and instructions are provided as a zipped folder. |
| Open Datasets | No | The paper describes generating samples using specific distributions (e.g., Wishart distribution) but does not provide access information (link, citation, or repository) for a publicly available or open dataset used in the experiments. |
| Dataset Splits | No | The paper performs simulations and discusses sample sizes but does not specify training, validation, or test dataset splits or cross-validation methods. |
| Hardware Specification | Yes | Simulations are done in Matlab on a desktop computer with an Intel Xeon processor at 3.60GHz with 31.9 GB of RAM running Windows 10. |
| Software Dependencies | No | The paper mentions 'Matlab' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | No | The paper describes aspects of the numerical examples, such as data generation parameters and the type of algorithm used (e.g., gradient descent), but does not provide specific hyperparameter values or detailed system-level training settings for these algorithms. |