Nonconvex Optimization for Regression with Fairness Constraints
Authors: Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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). |