Differentially Private Post-Processing for Fair Regression
Authors: Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we empirically explore the trade-offs achieved by our post-processing algorithm on Law School and Communities & Crime datasets. |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign 2University of Waterloo and Vector Institute. |
| Pseudocode | Yes | Algorithm 1 Fair and Private Post-Processing (Attribute-Aware) |
| Open Source Code | Yes | Code is available at https://github.com/rxian/fair-regression. |
| Open Datasets | Yes | Communities & Crime (Redmond & Baveja, 2002). ... Law School (Wightman, 1998). |
| Dataset Splits | No | The paper states, 'The datasets are randomly split 70-30 for training (i.e., post-processing) and testing,' but does not provide details for a separate validation split used in their specific experimental setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not explicitly list any software dependencies with specific version numbers required for reproducibility. |
| Experiment Setup | Yes | Algorithm 1... requires specifying an interval [s, t], number of bins k, fairness tolerance α, privacy budget ε. |