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..
KNG: The K-Norm Gradient Mechanism
Authors: Matthew Reimherr, Jordan Awan
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to theoretical guarantees on privacy and utility, we con๏ฌrm the utility of KNG empirically in the settings of linear and quantile regression through simulations. |
| Researcher Affiliation | Academia | Matthew Reimherr Department of Statistics Pennsylvania State University State College, PA 16802 EMAIL Jordan Awan Department of Statistics Pennsylvania State University State College, PA 16802 EMAIL |
| Pseudocode | Yes | Algorithm 1 Regression Simulation |
| Open Source Code | No | The paper discusses the implementation of sampling procedures (e.g., MCMC) but does not provide any link or explicit statement about releasing the source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for simulations using specific distributions (e.g., Xij iid U(-1, 1), errors ei N(0, 1)) rather than using a publicly available or open dataset. |
| Dataset Splits | No | The paper conducts simulations by generating data for each replicate but does not describe the use of explicit training, validation, and test dataset splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a 'one-at-a-time MCMC procedure' but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow, or specific statistical packages) used for the implementation. |
| Experiment Setup | Yes | For each n in 10^2, 10^3, 10^4, . . . , 10^7 we run 100 replicates of Algorithm 1 at ฯต = 1. For KNG and exponential mechanism, we draw samples using a one-at-a-time MCMC procedure with 10000 steps. |