Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Latent Feature Augmentation for Fairness
Authors: Hoin Jung, Junyi Chai, Xiaoqian Wang
ICLR 2025 | Venue PDF | LLM Run Details | Input Tokens: 36,498 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,738 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across diverse datasets, modalities, and backbone networks validate that training with these adversarial features significantly enhances fairness while maintaining predictive accuracy in classification tasks. The code is available on Git Hub. |
| Researcher Affiliation | Academia | Hoin Jung, Junyi Chai & Xiaoqian Wang Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette, IN, 47907, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Adversarial Latent Feature Augmentation |
| Open Source Code | Yes | The code is available on Git Hub. |
| Open Datasets | Yes | In this paper, we use four different tabular datasets Adult (Dua et al., 2017), COMPAS (Jeff Larson & Angwin, 2016), German (Dua et al., 2017), and Drug (Dua et al., 2017). Also Celeb A (Liu et al., 2018) and Wikipedia Toxicity (Thain et al., 2017) datasets are used for verify the performance of the proposed method in image and text classification, respectively. |
| Dataset Splits | Yes | All datasets are split into 60:20:20 for train, validation, and test subset, respectively. |
| Hardware Specification | Yes | CPU AMD Ryzen Threadripper 3960X 24-Core Processor GPU NVIDIA Ge Force RTX 3090 |
| Software Dependencies | No | During the pre-training, we choose the best parameter when the validation accuracy is the highest. In the attacking step, parameters of both the encoder and classifier are fixed, while only the last layer is newly initialized for fine-tuning with the augmented latent features. The different learning rates are used in each step, Adam optimizer with learning rate 1e 3 in pre-training and fine-tuning, Adam optimizer with learning rate 0.1 in adversarial attack. |
| Experiment Setup | Yes | To verify our approach, we apply our method to two base classifiers for tabular datasets, Logistic Regression and MLP with Re LU activation function and two hidden layers of 128 dimensions. For the Celeb A dataset, we adopt Res Net-50 (He et al., 2016), Vi T (Dosovitskiy, 2020), and Swin Transformer (Liu et al., 2021) as baselines. For the Wiki dataset, we use LSTM (Hochreiter & Schmidhuber, 1997), BERT (Devlin, 2018), and Distill BERT (Sanh, 2019) as baselines. During the pre-training, we choose the best parameter when the validation accuracy is the highest. In the attacking step, parameters of both the encoder and classifier are fixed, while only the last layer is newly initialized for fine-tuning with the augmented latent features. The different learning rates are used in each step, Adam optimizer with learning rate 1e 3 in pre-training and fine-tuning, Adam optimizer with learning rate 0.1 in adversarial attack. For each experiment, we take the result when the validation accruacy is the highest. For a fair comparison, we train each case 10 times and report the mean and the standard deviation for tabular datasets and text dataset. |