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 [1].
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. |