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..
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training
Authors: Yuji Roh, Kangwook Lee, Steven Whang, Changho Suh
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results for FR-Train. For the fairness measure, we use disparate impact, while leaving in the supplementary the results for equalized odds and equal opportunity. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea 2Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, Wisconsin, USA. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/yuji-roh/fr-train |
| Open Datasets | Yes | We use two real datasets: Pro Publica COMPAS (Angwin et al., 2016) and Adult Census (Kohavi, 1996), which have 7,214 and 45,222 examples, respectively. |
| Dataset Splits | Yes | To make a validation set, we randomly select clean examples that amount to 10% of the entire training data. For FR-Train and RML, the validation set is 10% of Dtr. We consider a scenario where one first constructs a small (which amounts to 5% of Dtr) validation set based on crowdsourcing |
| Hardware Specification | Yes | We use Py Torch (Paszke et al., 2017), and all experiments are performed on a server with Intel i7-6850 CPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al., 2017)' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Here λ1 and λ2 are tuning knobs that play roles to emphasize fair and robust training, respectively. We compute the final example weights as W = R + D(X, Z, ˆY ) (1 R) where R = σ( Lc /Ld C) is a conversion of the loss ratio into a probability using the sigmoid function σ and hyperparameter C. |