Policy Learning for Fairness in Ranking
Authors: Ashudeep Singh, Thorsten Joachims
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we provide empirical results on simulated and real-world datasets verifying the effectiveness of the approach in individual and group-fairness settings. |
| Researcher Affiliation | Academia | Ashudeep Singh Department of Computer Science Cornell University Ithaca, NY 14850 ashudeep@cs.cornell.edu Thorsten Joachims Department of Computer Science Cornell University Ithaca, NY 14850 tj@cs.cornell.edu |
| Pseudocode | Yes | Algorithm 1 summarizes our method for learning fair ranking policies given a training dataset. |
| Open Source Code | No | The paper does not explicitly state that source code for their methodology is available, nor does it provide a link to a repository. |
| Open Datasets | Yes | We conduct experiments on the Yahoo dataset [24]. ... For Individual Fairness, we train FAIR-PG-RANK with a linear and a neural network model on the Yahoo! Learning to rank challenge dataset... For Group fairness, we adapt the German Credit Dataset from the UCI repository [39] to a learning-to-rank task (described in the supplementary), choosing gender as the group attribute. |
| Dataset Splits | No | Algorithm 1 mentions 'until convergence on the validation set', and states 'We use the standard experiment setup on the SET 1 dataset' for Yahoo, but the main text does not specify explicit split percentages or counts for the validation set. Details are deferred to supplementary material. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a linear model and a neural network, but it does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | No | The paper states 'Details of the models and training hyperparameters are given in the supplementary material,' indicating that specific experimental setup details, such as hyperparameter values, are not provided in the main text. |