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..
Regression under demographic parity constraints via unlabeled post-processing
Authors: Gayane Taturyan, Evgenii Chzhen, Mohamed Hebiri
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm is backed by finite-sample analysis and post-processing bounds, with experimental results validating our theoretical findings. |
| Researcher Affiliation | Collaboration | Gayane Taturyan IRT System X, Université Gustave Eiffel, Université Paul-Sabatier EMAIL Evgenii Chzhen CNRS, Université Paris-Saclay EMAIL Mohamed Hebiri Université Gustave Eiffel EMAIL |
| Pseudocode | Yes | Algorithm 1: DP post-processing(L, T, β, p, B, η, τ) |
| Open Source Code | Yes | The code is available at https://github.com/taturyan/unaware-fair-reg. |
| Open Datasets | Yes | We conduct our study on two datasets: Law School dataset (Wightman (1998)) and Communities and Crime dataset (Redmond (2009)). In both datasets, ethnicity is a sensitive attribute [...] Additional experiments on Adult dataset (Lichman (2013)). |
| Dataset Splits | Yes | First, we randomly split the data into training, unlabeled and testing sets with proportions of 0.4 0.4 0.2. |
| Hardware Specification | Yes | The experiments are conducted on a Processor 11th Gen Intel(R) Core(TM) i7-1195G7 2.90GHz with 16GB RAM. |
| Software Dependencies | No | We use simple Linear Regression and Logistic Regression from scikit-learn for training the regressor and the classifier. While 'scikit-learn' is mentioned, no specific version number is provided for it or any other key software component. |
| Experiment Setup | Yes | We illustrate the convergence for ε = (2 8, 2 8) unfairness threshold. [...] We train Communities and Crime dataset for N=15000 iterations and Law School dataset for N=5000 iterations for each pair of epsilons. We use parameters L = T and B = 1 for both datasets. |