Smooth Flipping Probability for Differential Private Sign Random Projection Methods
Authors: Ping Li, Xiaoyun Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we conduct retrieval and classification experiments on benchmark datasets. |
| Researcher Affiliation | Industry | Ping Li, Xiaoyun Li Linked In Ads 700 Bellevue Way NE, Bellevue, WA 98004, USA {pingli98, lixiaoyun996}@gmail.com |
| Pseudocode | Yes | Algorithm 1: DP-RP-G and DP-RP-G-OPT |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for their described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We first test the methods in similarity search problems, using two standard image retrieval datasets, MNIST [53] and CIFAR [51]. |
| Dataset Splits | No | The paper mentions using a 'training set' and 'test set' but does not provide specific details on validation dataset splits, percentages, or methodology like cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions 'SVM [20]' which refers to LIBSVM, but does not provide a specific version number. No other key software components with version numbers are listed. |
| Experiment Setup | No | The paper mentions parameters like `k` and `ϵ` for experiments and `t` for repetitions, but it does not provide specific details on hyperparameters such as learning rate, batch size, optimizer settings, or model initialization for the classification or retrieval tasks. |