A General Approach to Fairness with Optimal Transport
Authors: Chiappa Silvia, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, Aslanides John3633-3640
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate its performance on several benchmark fairness datasets. Overall, our methods reach higher fairness with lower loss in accuracy. |
| Researcher Affiliation | Collaboration | 1Deep Mind London, 2UC Berkeley, 3Google Research |
| Pseudocode | Yes | Algorithm 1 Wass-p Geodesic |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We evaluated our approach on the National Longitudinal Survey of Youth (NLSY) 1979 regression dataset, on the UCI Communities & Crime (C&C) (Lichman 2013) regression dataset, on the Law School Admission Council (LSAC) regression dataset, and on the UCI Adult binary classification dataset. |
| Dataset Splits | No | The paper mentions 'test performance' and training, but does not explicitly provide details on train/validation/test dataset splits (percentages, counts, or specific predefined splits) within the main text. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions tuning hyperparameters and defining loss functions, but does not provide specific values for hyperparameters or detailed training configurations in the main text. |