Fairness and Accuracy under Domain Generalization
Authors: Thai-Hoang Pham, Xueru Zhang, Ping Zhang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world data validate the proposed algorithm. |
| Researcher Affiliation | Academia | Thai-Hoang Pham, Xueru Zhang, Ping Zhang The Ohio State University, Columbus, OH 43210, USA {pham.375,zhang.12807,zhang.10631}@osu.edu |
| Pseudocode | Yes | Algorithm 1: Fairness and Accuracy Transfer by Density Matching (FATDM) |
| Open Source Code | Yes | Model implementation is available at https://github.com/pth1993/FATDM. |
| Open Datasets | Yes | The original chest X-ray images and the corresponding metadata can be downloaded from PhysioNet (https://physionet.org/content/mimic-cxr-jpg/2.0.0/; https: //physionet.org/content/mimiciv/2.0/). |
| Dataset Splits | Yes | We follow leave-one-out domain setting in which 3 domains are used for training and the remaining domain serves as the unseen target domain and is used for evaluation. ... 10% of training data is used for validation. Each model is trained with 10 epoches and the results are from the epoch with best performance on the validation set. |
| Hardware Specification | Yes | Models (FATDM and baselines) are implemented by PyTorch library version 1.11 and is trained on multiple computer nodes (each model instance is trained on a single node which has 4 CPUs, 8GB of memory, and a single GPU (P100 or V100)). |
| Software Dependencies | Yes | Models (FATDM and baselines) are implemented by PyTorch library version 1.11 |
| Experiment Setup | Yes | ω (hyper-parameter that controls accuracy-fairness trade-off) varies from 0 to 10 with step sizes 0.0002 for ω [0, 0.002], 0.002 for ω [0.002, 0.1] and 0.2 for ω [0.2, 10], and γ (hyper-parameter that controls accuracy-invariance trade-off) is set to 0.1 (after hyper-parameter tuning). Models (FATDM and baselines) are implemented by PyTorch library version 1.11 and is trained on multiple computer nodes (each model instance is trained on a single node which has 4 CPUs, 8GB of memory, and a single GPU (P100 or V100)). One domain s data is used for testing and the other domains data is used for training (10% of training data is used for validation). Each model is trained with 10 epoches and the results are from the epoch with best performance on the validation set. |