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.
Differentially Private Post-Processing for Fair Regression
Authors: Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,369 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,567 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 | 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. |