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..
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
Authors: Zhenhuan Yang, Yan Lok Ko, Kush R. Varshney, Yiming Ying
AAAI 2023 | Venue PDF | 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. |