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].
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Authors: Borja Balle, Gilles Barthe, Marco Gaboardi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we present a general method that recovers and improves prior analyses, yields lower bounds and derives new instances of privacy amplification by subsampling. Our method leverages a characterization of differential privacy as a divergence which emerged in the program verification community. |
| Researcher Affiliation | Collaboration | Borja Balle Amazon Research EMAIL Gilles Barthe IMDEA Software Institute EMAIL Marco Gaboardi University at Buffalo, SUNY EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details like hyperparameters or training configurations. |