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..
SoFaiR: Single Shot Fair Representation Learning
Authors: Xavier Gitiaux, Huzefa Rangwala
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we find on three datasets that So Fai R achieves similar fairness-information tradeoffs as its multi-shot counterparts. |
| Researcher Affiliation | Academia | Xavier Gitiaux and Huzefa Rangwala George Mason University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code publicly available here2. 2See https://github.com/Gitiauxx/So Fai R |
| Open Datasets | Yes | We validate our single-shot approach with three benchmark datasets: DSprite-Unfair, Adults and Heritage. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide specific details about validation dataset splits (e.g., percentages, sample counts) in the main text. |
| Hardware Specification | Yes | We perform the experiment on a AMD Ryzen Threadripper 2950X 16-Core Processor CPU and a NVIDIA GV102 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | Architecture details and hyperparameter values are in the supplementary file. |