Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
Authors: Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that Fair DRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical and Computer Engineering, Seoul National University 2 NAVER AI Lab 3 ASRI/INMC/IPAI/AIIS, Seoul National University |
| Pseudocode | Yes | Algorithm 1: Fair DRO Iterative Optimization |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | Two tabular datasets, UCI Adult (Dua et al., 2017) (Adult) and Pro Publica COMPAS (Julia Angwin & Kirchner, 2016) (COMPAS), are used for the benchmark. We also evaluate Fair DRO on UTKFace (Zhang et al., 2017), a face dataset with multi-class and multi-group labels. Civil Comments-WILDS (Koh et al., 2021). The description and results on another vision dataset, Celeb A (Liu et al., 2015), are given in Appendix D.2. |
| Dataset Splits | No | The paper mentions evaluating models on "separate test sets" but does not explicitly provide details about training/validation/test dataset splits, percentages, or specific counts for a validation set. |
| Hardware Specification | Yes | Experiments are performed on a server with AMD Ryzen Threadripper PRO 3975WX CPUs and NVIDIA RTX A5000 GPUs. |
| Software Dependencies | No | The paper states, "We used Py Torch (Paszke et al., 2019)", but it does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For tabular and vision datasets, we train all models with the Adam W optimizer (Loshchilov & Hutter, 2019) for 70 epochs. We set the mini-batch size and the weight decay as 128 and 0.001, respectively. The initial learning rate is set as 0.001 and decayed by cosine annealing technique (Loshchilov & Hutter, 2017). For the language dataset, we fine-tune pre-trained BERT with the Adam W optimizer for 3 epochs. We set the mini-batch size and the weight decay as 24 and 0.001, respectively. The initial learning rate is set as 0.00002 and adjusted with a learning rate schedule using a warm-up phase followed by a linear decay. |