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.
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
Authors: Kristjan Greenewald, Yuancheng Yu, Hao Wang, Kai Xu
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 29,045 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,505 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments demonstrate that our approach can generate synthetic data of higher quality compared with baselines. |
| Researcher Affiliation | Collaboration | Kristjan Greenewald MIT-IBM Watson AI Lab, IBM Research EMAIL Yuancheng Yu UIUC EMAIL Hao Wang MIT-IBM Watson AI Lab, IBM Research EMAIL Kai Xu MIT-IBM Watson AI Lab, IBM Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Training DP generative modes with the smoothed-sliced f-divergence. |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement about releasing the code for the work described in this paper. |
| Open Datasets | Yes | We validate both our method and baselines using the US Census data derived from the American Community Survey (ACS) Public Use Microdata Sample (PUMS). Using the API of the Folktables package [DHMS21], we access the 2018 California data. |
| Dataset Splits | No | The paper mentions training and testing data, and subsampling for privacy amplification, but does not explicitly provide details for a validation split or its proportion. |
| Hardware Specification | Yes | For our method and baselines, each model was trained using a V100 GPU, with runtimes typically less than 2 hours for our method (200 epochs). |
| Software Dependencies | No | The paper mentions using 'open-source Python library [Sma23]' and 'Folktables package [DHMS21]' but does not provide specific version numbers for Python, PyTorch, or other key software dependencies. |
| Experiment Setup | Yes | For our method and Slice Wass, all experiments used batch size of 128 and learning rate 2 × 10−5, and ran for 200 epochs. |