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.
Composition Theorems for Interactive Differential Privacy
Authors: Xin Lyu
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,916 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,052 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 | Our work is purely theoretical, and we do not see any negative societal impacts it may cause. |
| Researcher Affiliation | Academia | Xin Lyu Department of Electrical Engineering and Computer Science University of California, Berkeley Berkeley, CA, 94720 EMAIL |
| Pseudocode | No | The paper presents theoretical proofs and definitions but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'Our work is purely theoretical'. There is no mention of releasing source code for the methodology described. The 'Author Checklist' explicitly marks 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?' as [N/A]. |
| Open Datasets | No | The paper is purely theoretical and does not involve experiments on datasets. Therefore, no public dataset information is provided. |
| Dataset Splits | No | The paper is purely theoretical and does not involve experiments with data splits. The author checklist marks 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?' as [N/A]. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup that would require hardware specifications. The author checklist marks 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?' as [N/A]. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any experimental setup that would require software dependencies with version numbers. The author checklist marks 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?' as [N/A]. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details, hyperparameters, or training settings. The author checklist marks 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?' as [N/A]. |