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..
Smooth Flipping Probability for Differential Private Sign Random Projection Methods
Authors: Ping Li, Xiaoyun Li
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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. |