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..
Generating Private Synthetic Data with Genetic Algorithms
Authors: Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that on data with both discrete and real-valued attributes, PRIVATE-GSD outperforms the state-of-the-art methods on nondifferential queries while matching accuracy in approximating differentiable ones. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2Peking University 3University of Minnesota. |
| Pseudocode | Yes | Algorithm 1 Private Genetic Algorithm for Synthetic Data (PRIVATE-GSD) |
| Open Source Code | Yes | The PRIVATE-GSD source code is publicly available at https: //github.com/giusevtr/private_gsd. |
| Open Datasets | Yes | For our empirical evaluation, we use datasets derived from the Folktables package (Ding et al., 2021), which defines datasets using samples from the American Community Survey (ACS). |
| Dataset Splits | No | For the main experiments, the paper mentions using datasets from the Folktables package but does not specify any training/validation/test splits. For the ML evaluation in Appendix C, it states 'dividing each dataset into a training and test set, using an 80/20 partition,' but no separate validation split is mentioned. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'Folktables package (Ding et al., 2021)' but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Table 7. Hyperparameters experiments (with adaptivity). lists detailed hyperparameter values such as Data Size (N), Pmut, Pcross, Elite Size, Max Generations, Queries Sampled (K), Learning Rate, Inverse Temp. (Οit), # Product Mixtures (K), Batch Size (B), Max Iterations (M), # Samples, and T (adaptive epochs) for various methods. |