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 [1].
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. |