Minimax Pareto Fairness: A Multi Objective Perspective
Authors: Natalia Martinez, Martin Bertran, Guillermo Sapiro
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches. |
| Researcher Affiliation | Academia | 1Department of Electircal and Computer Engineering, Duke University. |
| Pseudocode | Yes | Algorithm 1 APStar |
| Open Source Code | Yes | Code is available at github.com/natalialmg/MMPF. |
| Open Datasets | Yes | In addition to this, we demonstrate how our methodology performs on real tasks such as inferring income status in the Adult dataset (Dua & Graff, 2017a), predicting ICU mortality rates in the MIMIC-III dataset from hospital notes (Johnson et al., 2016), classifying skin lesions in the HAM10000 dataset (Tschandl et al., 2018), and assessing credit risk on the German Credit dataset (Dua & Graff, 2017b). |
| Dataset Splits | No | The paper mentions 'standard deviations computed across 5 splits' but does not provide specific percentages or details about a dedicated validation set used for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper mentions training DNNs and neural networks but does not provide specific details on the hardware used, such as GPU/CPU models, memory, or cloud instances. |
| Software Dependencies | No | The paper mentions 'Pytorch code' and 'Stochastic Gradient Descent' (SGD) but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Classifiers are implemented using neural networks and/or linear logistic regression; for details on architectures and hyperparameters, refer to Section A.9. |