On Differentially Private Sampling from Gaussian and Product Distributions
Authors: Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present new DP sampling algorithms, and show that they achieve near-optimal sample complexity in the first two settings. |
| Researcher Affiliation | Collaboration | Badih Ghazi Google Research Mountain View, CA, US EMAIL Xiao Hu University of Waterloo Waterloo, Canada EMAIL Ravi Kumar Google Research Mountain View, CA, US EMAIL Pasin Manurangsi Google Research Bangkok, Thailand EMAIL |
| Pseudocode | Yes | Algorithm 1 SPHERICALGAUSSIANSAMPLER Parameters: B, σ > 0, and n N. Sample X1, . . . , Xn D for i = 1, . . . , n do Xtrunc i = trunc2 B(Xi) see (1) Sample Z N(0, σ2I) return Z + 1 i [n] Xtrunc i |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |