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..
Conformalized Fairness via Quantile Regression
Authors: Meichen Liu, Lei Ding, Dengdeng Yu, Wulong Liu, Linglong Kong, Bei Jiang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superior empirical performance of this approach on several benchmark datasets. |
| Researcher Affiliation | Collaboration | 1Department of Mathematical and Statistical Sciences, University of Alberta 2Department of Mathematics, University of Texas at Arlington 3 Huawei Noah s Ark Lab Canada |
| Pseudocode | Yes | We present the pseudo-codes of CFQP as well as the construction of ˆgα for Eq. 9 in Algorithm 1, 2 respectively. |
| Open Source Code | Yes | The code for reproducing our results is avaiable at https: //github.com/Lei-Ding07/Conformal_Quantile_Fairness. |
| Open Datasets | Yes | We report the performance of post-processing fairness adjustment on quantiles through four benchmark datasets: Law School (LAW), Community&Crime (CRIME), MEPS 2016 (MEPS), Government Salary (GOV). |
| Dataset Splits | Yes | We split the training data into proper training and calibration sets of equal sizes. |
| Hardware Specification | No | The ethics checklist explicitly states 'N/A' for hardware specifications: 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]' |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the provided text. |
| Experiment Setup | No | While the nominal miscoverage rate α is mentioned as 0.1, other specific hyperparameters like learning rates, batch sizes, or optimizer settings for the linear, random forest, and neural network models are not detailed in the provided text. |