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..
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. |