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..
Sample Selection for Fair and Robust Training
Authors: Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our algorithm obtains fairness and robustness that are better than or comparable to the state-of-the-art technique, both on synthetic and benchmark real datasets. |
| Researcher Affiliation | Academia | Yuji Roh KAIST EMAIL Kangwook Lee University of Wisconsin-Madison EMAIL Steven Euijong Whang KAIST EMAIL Changho Suh KAIST EMAIL |
| Pseudocode | Yes | Algorithm 1: Greedy-Based Clean and Fair Sample Selection |
| Open Source Code | No | The paper does not include an explicit statement or a direct link to the source code for the methodology described. |
| Open Datasets | Yes | We utilize two real datasets, Pro Publica COMPAS [Angwin et al., 2016] and Adult Census [Kohavi, 1996], and use the same pre-processing in IBM Fairness 360 [Bellamy et al., 2019]. |
| Dataset Splits | No | The paper mentions evaluating on "separate clean test sets" and minibatches, but does not specify the train/validation/test splits (e.g., percentages or sample counts) for its experiments. |
| Hardware Specification | Yes | all experiments are run on Intel Xeon Silver 4210R CPUs and NVIDIA Quadro RTX 8000 GPUs. |
| Software Dependencies | Yes | Our algorithm and Fair Batch are implemented in PyTorch [Paszke et al., 2019] with Python 3.8. |
| Experiment Setup | Yes | We train our models using stochastic gradient descent (SGD) with a batch size of 200, momentum of 0.9, and learning rate of 0.001 for 3,000 epochs. |