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..
Mean Estimation in the Add-Remove Model of Differential Privacy
Authors: Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 3 we plot the empirical performance of the algorithms discussed in Sections 3 and 4.1 on synthetic datasets and explore how the performance changes with parameters such as the privacy budget ε and the true mean µ. The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. All datasets have 10, 000 points, and mean squared error is computed over 100, 000 runs of each algorithm. |
| Researcher Affiliation | Industry | 1Google Research, NYC. |
| Pseudocode | Yes | Algorithm 1 Independent noise addition. Input: Multiset D [l, u], ε > 0. ... Algorithm 2 Shifted noise addition. Input: Multiset D [l, u], ε > 0. ... Algorithm 3 Transformed noise addition. Input: Multiset D [l, u], ε > 0. |
| Open Source Code | No | No explicit statement or link indicating the provision of open-source code for the methodology described in this paper. |
| Open Datasets | No | The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing is provided. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. |
| Experiment Setup | Yes | The underlying datasets are generated i.i.d. with varying µ in the range [ℓ= 0, u = 1]. All datasets have 10, 000 points, and mean squared error is computed over 100, 000 runs of each algorithm. |