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 Post-Processing for Fair Regression
Authors: Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
ICML 2024 | Venue PDF | 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. |