Empirical Risk Minimization Under Fairness Constraints
Authors: Michele Donini, Luca Oneto, Shai Ben-David, John S. Shawe-Taylor, Massimiliano Pontil
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches. We report numerical experiments using both linear and nonlinear kernels, which indicate that our method improves on the state-of-the-art in four out of five datasets and is competitive on the fifth dataset. |
| Researcher Affiliation | Academia | 1Istituto Italiano di Tecnologia (Italy) 2University of Genoa (Italy), 3University of Waterloo (Canada), 4University College London (UK) |
| Pseudocode | No | The paper describes mathematical formulations and derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of our method is available at: https://github.com/jmikko/fair_ERM. |
| Open Datasets | Yes | We next compare the performance of our model to set of different methods on 5 publicly available datasets: Arrhythmia, COMPAS, Adult, German, and Drug. A description of the datasets is provided in the supplementary material. These datasets have been selected from the standard databases of datasets (UCI, mldata and Fairness-Measures5). |
| Dataset Splits | Yes | In all experiments, we performed a 10-fold cross validation (CV) to select the best hyperparameters4. For the Arrhythmia, COMPAS, German and Drug datasets, this procedure is repeated 10 times, and we reported the average performance on the test set alongside its standard deviation. For the Adult dataset, we used the provided split of train and test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Lasso algorithm' and refers to 'Python code' from a third-party method for comparison, but it does not specify version numbers for any software dependencies or libraries used in their own experiments. |
| Experiment Setup | Yes | We trained different models, varying the value of the hyperparameter C, and using the standard linear SVM and our linear method. The regularization parameter C (for both SVM and our method) with 30 values, equally spaced in logarithmic scale between 10 4 and 104; we used both the linear or RBF kernel (i.e. for two examples x and z, the RBF kernel is e γ||x z||2) with γ 2 {0.001, 0.01, 0.1, 1}. |