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..
Improving the Variance of Differentially Private Randomized Experiments through Clustering
Authors: Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the theoretical and empirical performance of our CLUSTER-DP algorithm on both real and simulated data, comparing it to common baselines, including two special cases of our algorithm: its unclustered version and a uniformprior version. |
| Researcher Affiliation | Collaboration | 1Marshall School of Business, University of Southern California, Los Angeles, USA 2Google Research, New York, USA. Correspondence to: Adel Javanmard <EMAIL>, Jean Pouget-Abadie <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 UNIFORM-PRIOR-DP mechanism Algorithm 2 Our CLUSTER-DP mechanism |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, nor does it provide a link to a code repository. |
| Open Datasets | Yes | The You Tube social network dataset (Leskovec & Krevl, 2014) contains the friendship links of a set of users on You Tube, and the ground-truth clusters correspond to groups created by users. |
| Dataset Splits | No | The paper describes how data is generated and sampled for experiments (e.g., "super-population of three clusters of sizes 2.5e3, 5e3, and 10e4 units, and repeatedly draw uniformly at random sub-populations of three clusters from these original clusters"). For the You Tube dataset, it mentions "considering only the 50 largest communities". However, it does not provide specific training/test/validation dataset splits with percentages or counts, which are typically used for reproducing machine learning experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) needed to replicate the experiments. |
| Experiment Setup | Yes | Unless otherwise specified, and with no particular reason to fix parameters one way or another, we take K = 5, v = 5, and β = 4.5. We consider C = 3 clusters of sizes 500, 103, 2 103 with an equal number of controlled and treated units in each cluster. ... for CLUSTERDP mechanism, we set the truncation parameter γ = 0.02, the Laplace noise σ = 10, and the resampling probability λ = 0.8. ... For the CLUSTERDP and CLUSTER-FREE-DP, we set the Laplace parameter to σ = 10, and vary the truncation parameter γ [0.1/K, 1/K]. ... In the CLUSTER-DP mechanism, we set the truncation threshold to γ = 0.1/K and the Laplace noise level to σ = 5. |