Obtaining Fairness using Optimal Transport Theory
Authors: Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally application to simulated data in Section 5 enables to study the efficiency of the proposed procedures. |
| Researcher Affiliation | Academia | 1IMUVA, Universidad de Valladolid, Valladolid, Spain 2Institut de Math ematiques de Toulouse, Universit e Paul Sabatier, Toulouse, France. |
| Pseudocode | No | The paper describes the computational procedures in prose and mathematical notation within Section 4 but does not include a clearly labeled "Algorithm" or "Pseudocode" block. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the described methodology. |
| Open Datasets | No | The paper uses a simulated dataset, 'n0 = 600 and n1 = 400 examples from two multivariate normal distributions on R5', but does not provide concrete access information (link, DOI, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | Yes | splitting the set into the learning and the test sample using the ratio 300 / 700. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions machine learning models like 'logit classifier' and 'random forest classifier' but does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | we have chosen parameters β0 = (1, 1, 0.5, 1, 1, 1) and β1 = (1, 0.4, 1, 1, 1, 0.5) to build a logit model for each group with different probability of success for s = 0, 1... |