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..
Nonconvex Optimization for Regression with Fairness Constraints
Authors: Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is empirically evaluated by four real-world datasets. Unlike most methods, our method is capable of considering the possibly non-linear interaction of numeric sensitive attributes with the target variable. As we consider nonconvexity that naturally arises in measuring a correlation between s and y, we think this result is a first step that ties the study of nonconvex optimization in the context of fairness-aware machine learning. Figure 2 shows the results of our simulations. |
| Researcher Affiliation | Academia | 1The University of Tokyo, Tokyo, Japan. 2RIKEN AIP, Tokyo, Japan. 3Santa Fe Institute, New Mexico, United States. |
| Pseudocode | No | The paper describes mathematical formulations for its optimization problems (e.g., SDP, QCQP) but does not include a distinct pseudocode block or algorithm section. |
| Open Source Code | Yes | The source code used in the simulation is available at https://github.com/jkomiyama/fairregresion. |
| Open Datasets | Yes | The Communities and Crime (C&C) dataset, The COMPAS dataset (Angwin et al., 2016), The National Longitudinal Survey of Youth (NLSY) dataset6, The Law School Admissions Council (LSAC) dataset7. Footnote 6: https://www.bls.gov/nls/. Footnote 7: http://www2.law.ucla.edu/sander/Systemic/Data.htm. |
| Dataset Splits | Yes | We split the data into 5-folds: One was for validation dataset that was used to optimize the hyperparameters, and another was for the test dataset. The resting three folds were the training dataset. |
| Hardware Specification | No | The simulation here was conducted by using modern Xeon-core PC servers. While this specifies a type of processor, it lacks specific model numbers or detailed hardware specifications such as GPU models or memory amounts. |
| Software Dependencies | No | We solved the convex QCQP optimization by using the Gurobi optimizer. While Gurobi is named, no specific version number for Gurobi or any other software dependency is provided. |
| Experiment Setup | Yes | The hyperparameters were optimized in validation datasets among λ = {1.0, 10.0, 100.0} and γ = {0.1, 1.0, 10.0, 100.0}, where γ was the hyper-parameter of the RBF kernel K(x, y) = exp( γ(x y)2). |