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..
Differentially Private Covariance Revisited
Authors: Wei Dong, Yuting Liang, Ke Yi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that they offer significant improvements over prior work. |
| Researcher Affiliation | Academia | Wei Dong, Yuting Liang, Ke Yi EMAIL Department of Computer Science Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 Separate Cov |
| Open Source Code | Yes | The code can be found at https://github.com/hkust DB/Private Covariance. |
| Open Datasets | Yes | The first dataset is the MNIST [27] dataset, which contains images of handwritten digits. We use its training dataset which contains 60, 000 images represented as vectors in Zd 255, where d 784 28 ˆ 28. These vectors are normalized by 255 ? d in the experiments. ... [27] Yann Le Cun, Corinna Cortes, and Christopher J.C. Burges. The mnist database of handwritten digits, 1998. Available online at: http://yann.lecun.com/exdb/mnist/. Last accessed: May. 2022. |
| Dataset Splits | No | The paper mentions using a 'training dataset' but does not specify any train/validation/test splits or cross-validation methodology for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions implementation in Python and the use of the 'scikit-learn package' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The ρ here is fixed at 0.1 and we examine the error growth w.r.t. d for n 1000, 4000, 16000. ... default values d 200, n 50000, N 4 and ρ 0.1 ... Each experiment is repeated 50 times, and we report the average error. ... we scale all datasets such that 0.5 ď radp Xq ď 1. ... The parameter s characterizes the skewness, which we fix as s 3. |