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..
On Sparse Linear Regression in the Local Differential Privacy Model
Authors: Di Wang, Jinhui Xu
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world and synthetic datasets conο¬rm our theoretical analysis. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, USA. Emails: EMAIL. |
| Pseudocode | Yes | Algorithm 1 LDP-IHT |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using a 'real world dataset Covertype' but does not provide a formal citation, link, or repository information for accessing it. It also uses synthetic data whose generation process is described, but no public access is provided. |
| Dataset Splits | No | The paper describes data generation but does not provide specific training/validation/test dataset splits, percentages, or sample counts, nor does it mention cross-validation or standard benchmark splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions using 'TFOCS' as a method for choosing the step size but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | We assume νΆ= 0.05 in our experiment. We run algorithms Label-LDP-IHT with ν= 0.2 or ν= 0.1, ν = ν , ν= log ν ν , νΏ= 10 3 and a random normal Gaussian vector as the initial point to obtain νν. |