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..
Differentially Private Normalizing Flows for Synthetic Tabular Data Generation
Authors: Jaewoo Lee, Minjung Kim, Yonghyun Jeong, Youngmin Ro7345-7353
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations show that the proposed model preserves statistical properties of original dataset better than other baselines. |
| Researcher Affiliation | Collaboration | 1University of Georgia 2Samsung SDS EMAIL, 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 | Yes | To evaluate the performance of DP-HFlow, we perform experiments on four real datasets: Adult, Census, Covertype, Intrusion. |
| Dataset Splits | No | The paper mentions evaluating on a 'testing' set but does not provide specific details on training, validation, and test splits (e.g., percentages or counts) or a general splitting methodology. |
| Hardware Specification | Yes | All experiments were performed on a server with an NVIDIA RTX 8000 GPU. |
| Software Dependencies | No | The paper mentions "Ada Belief optimizer (Zhuang et al. 2020)" but does not specify software names with version numbers for replication. |
| Experiment Setup | Yes | In all experiments, DP-HFlow is instantiated by stacking 3 blocks of autoregressive spline transformation and low rank-based linear transformation on top of dequantization layer. A reverse ordering permutations is inserted in between blocks. We used Ada Belief optimizer (Zhuang et al. 2020) with learning rate 0.001 and default smoothing parameter of β1 = 0.9 and β2 = 0.999. |