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..
Principal Component Analysis in the Local Differential Privacy Model
Authors: Di Wang, Jinhui Xu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real world datasets con๏ฌrm the theoretical guarantees of our algorithms. |
| Researcher Affiliation | Academia | Di Wang , Jinhui Xu Department of Computer Science and Engineering State University of New York at Bu๏ฌalo, NY, USA. {dwang45,jinhui}@bu๏ฌalo.edu. |
| Pseudocode | Yes | Algorithm 1 Local Gaussian Mechanism and Algorithm 2 Local Gaussian Mechanism-High Dimension |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or links to a code repository. |
| Open Datasets | Yes | For real world datasets, we run Algorithm 1 on Covertype and Buzz datasets [Dheeru and Karra Taniskidou, 2017] with normalized rows for each dataset. ... UCI machine learning repository, 2017. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or testing splits for the datasets used in the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | We choose ํ= 105, ํ= 40, ํ= {5, 10, 15, 20}, ํ= 0.5, ํฟ= 10 4, and ํ= 1. ... We set ํ= 10, ํ= 2000, ํ= 400, ํ = {15, 20, 40, 80} and ํ= 1. |