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..
Fair Multiple Decision Making Through Soft Interventions
Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments using both synthetic and real-world datasets show the effectiveness of our approach. |
| Researcher Affiliation | Academia | Yaowei Hu University of Arkansas EMAIL Yongkai Wu Clemson University EMAIL Lu Zhang University of Arkansas EMAIL Xintao Wu University of Arkansas EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Reproducibility. The source code and datasets are available at https://github.com/yaoweihu/Fair-Multiple-Decision-Making. |
| Open Datasets | Yes | For the real-world data, we use the Adult dataset [19] and build the causal graph by using the PC algorithm implemented in the Tetrad [25]. Reproducibility. The source code and datasets are available at https://github.com/yaoweihu/Fair-Multiple-Decision-Making. |
| Dataset Splits | Yes | The dataset is randomly split to training and testing datasets. obtained from 5-fold cross-validation. |
| Hardware Specification | Yes | All experiments are conducted in a PC with 8GB RAM and Intel Core i5-1035G1 CPU. |
| Software Dependencies | No | The paper mentions software like CVXPY, PyTorch, and Adam optimizer, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | By default, we use 0.05 as the threshold for judging fairness. For the joint method, since the objective function and constraints are non-convex, we add constraints as penalty terms to the objective function and adopt Py Torch [22] to optimize it using the Adam optimizer. |