Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Obtaining Fairness using Optimal Transport Theory
Authors: Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, Jean-Michel Loubes
ICML 2019 | Venue PDF | 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... |