Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
Authors: Borja Balle, Gilles Barthe, Marco Gaboardi
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,776 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,288 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |