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..
Self-Supervised Fair Representation Learning without Demographics
Authors: Junyi Chai, Xiaoqian Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method achieves better or comparable performance than state-of-the-art methods on three datasets in terms of accuracy and several fairness metrics.4 Experiments |
| Researcher Affiliation | Academia | Junyi Chai, Xiaoqian Wang Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47906 EMAIL |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Celeb A (Liu et al., 2015): The dataset contains...Adult (Dua and Graff, 2017) : The dataset contains...COMPAS (Larson et al., 2016): The dataset contains... |
| Dataset Splits | Yes | We repeat experiments on each dataset three times and report the average results and in each repetition we randomly spilt data into 64% training data, 16% validation data and 20% test data. |
| Hardware Specification | Yes | We implement our method in Py Torch 1.10.1 with one NVIDIA RTX-3090 GPU. |
| Software Dependencies | Yes | We implement our method in Py Torch 1.10.1 with one NVIDIA RTX-3090 GPU. |
| Experiment Setup | Yes | All hyperparameters are tuned to find the best validation accuracy. The values of hyperparameter in our method are set by performing cross-validation on training data in the value range of 0.1 to 10. The hyperparameters for the comparing methods are tuned as suggested in the original paper (Hardt et al., 2016; Hashimoto et al., 2018; Hwang et al., 2020). |