Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
Authors: Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm. |
| Researcher Affiliation | Collaboration | 1Etsy, Inc, Brooklyn, New York, USA 2University at Albany, State University of New York, Albany, New York, USA 3IBM Research, Yorktown Heights, New York, USA |
| Pseudocode | Yes | Algorithm 1: Minimax Fair AUC |
| Open Source Code | Yes | Implementation Details3 https://github.com/zhenhuan-yang/Minimax Fair AUC. |
| Open Datasets | Yes | We evaluate our algorithms on four datasets that have been commonly used in the fair machine learning literature (Zafar et al. 2017; Donini et al. 2018). ... The Adult dataset ... The Bank dataset ... The Compas dataset ... The Default dataset (Yeh and Lien 2009). |
| Dataset Splits | Yes | We partition the datasets to training, validation and testing in the ratio 60%:20%:20%. |
| Hardware Specification | No | The paper describes the models and datasets used but does not provide specific details on the hardware (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., specific libraries or frameworks like PyTorch, TensorFlow, or scikit-learn with their versions). |
| Experiment Setup | Yes | We partition the datasets to training, validation and testing in the ratio 60%:20%:20%. The batch size |B|, initial stepsizes ηθ 0, ηλ 0 and other hyperparameters are chosen based on the validation set. For Algorithm 1, early stopping is implemented based on the maximum group loss over the validation set. |