Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Wishart Mechanism for Differentially Private Principal Components Analysis
Authors: Wuxuan Jiang, Cong Xie, Zhihua Zhang
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this section, we are going to conduct theoretical analysis of Algorithms 1 and 2 under the framework of differential private matrix publishing. The theoretical support has two parts: privacy and utility guarantee. The former is the essential requirement for privacy-preserving algorithms and the latter tells how well the algorithm works against a nonprivate version. |
| Researcher Affiliation | Academia | Wuxuan Jiang, Cong Xie and Zhihua Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Laplace input perturbation |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access to any dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups involving dataset splits like training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |