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..
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
Authors: Jordan Awan, Jinshuo Dong
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | No experiments were run, as the results are justified by theory. |
| Researcher Affiliation | Academia | Jordan A. Awan Department of Statistics Purdue University EMAIL; Jinshuo Dong Department of Computer Science Northwestern University and IDEAL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It is a theoretical paper presenting mathematical definitions, theorems, and proofs. |
| Open Source Code | No | The paper states: 'No experiments were run, as the results are justified by theory.' Therefore, there is no source code for a methodology to be released. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets, training, or data splits. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets or validation splits. |
| Hardware Specification | No | The paper states: 'No experiments were run, as the results are justified by theory.' Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper states: 'No experiments were run, as the results are justified by theory.' Therefore, no software dependencies for experiments are listed. |
| Experiment Setup | No | The paper states: 'No experiments were run, as the results are justified by theory.' Therefore, no experimental setup details like hyperparameters or training settings are provided. |