Revisiting Differentially Private ReLU Regression
Authors: Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang, Di Wang, Jinhui Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world datasets also validate our results. |
| Researcher Affiliation | Academia | Meng Ding University at Buffalo Mingxi Lei University at Buffalo Liyang Zhu KAUST Shaowei Wang Guangzhou University Di Wang KAUST Jinhui Xu University at Buffalo |
| Pseudocode | Yes | Algorithm 1 DP-GLMtron, Algorithm 2 DP-Threshold, Algorithm 3 DP-TAGLMtron, Algorithm 4 DP-Tree-Aggregation |
| Open Source Code | No | All data and code will be released after acceptance. |
| Open Datasets | Yes | Additionally, due to space constraints, we present a real-data experiment on the MNIST dataset in Appendix B to demonstrate the performance of our proposed method. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions varying sample sizes (N) for experiments but does not detail how the data was partitioned for validation purposes. |
| Hardware Specification | No | The paper states 'The dimensionality of the data was fixed at 1,024' which refers to data characteristics, not hardware. The NeurIPS checklist indicates compute resources are in the appendix, but the appendix does not specify any particular hardware (GPU/CPU models, memory, etc.) used for experiments. |
| Software Dependencies | No | The paper mentions algorithms like DP-SGD, DP-FTRL, DP-GLMtron, and DP-TAGLMtron, and uses concepts like ReLU, Gaussian mechanism, and binary tree. However, it does not specify any software dependencies (e.g., libraries, frameworks, or operating systems) with version numbers. |
| Experiment Setup | Yes | The experiments were designed with varying privacy budgets (ε) set at 0.05, 0.2, and 0.5 with δ = 1 n1.1... The learning rate was initially set to 10 2, with N representing the sample size, which varied from 50 to 550 and increased in steps to 100. ... The dimensionality of the data was fixed at 1,024... The algorithms underwent a single iteration over generated data. ... For MNIST, the learning rate was initially set to 0.05, with N representing the sample size, which varied from 0 to 1000 and increased in steps to 100. |