Differentially Private Covariance Revisited
Authors: Wei Dong, Yuting Liang, Ke Yi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {wdongac,yliangbs,yike}@cse.ust.hk 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. |