Towards Counterfactual Fairness-Aware Domain Generalization in Changing Environments
Abstract
We introduce a relational approach to program synthesis. The key idea is to decompose synthesis tasks into simpler relational synthesis subtasks. Specifically, our representation decomposes a training input-output example into sets of input and output facts respectively. We then learn relations between the input and output facts. We demonstrate our approach using an off-the-shelf inductive logic programming (ILP) system on four challenging synthesis datasets. Our results show that (i) our representation can outperform a standard one, and (ii) an off-the-shelf ILP system with our representation can outperform domain-specific approaches.
Cite
Text
Lin et al. "Towards Counterfactual Fairness-Aware Domain Generalization in Changing Environments." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/504Markdown
[Lin et al. "Towards Counterfactual Fairness-Aware Domain Generalization in Changing Environments." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/lin2024ijcai-counterfactual/) doi:10.24963/ijcai.2024/504BibTeX
@inproceedings{lin2024ijcai-counterfactual,
title = {{Towards Counterfactual Fairness-Aware Domain Generalization in Changing Environments}},
author = {Lin, Yujie and Zhao, Chen and Shao, Minglai and Meng, Baoluo and Zhao, Xujiang and Chen, Haifeng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {4560-4568},
doi = {10.24963/ijcai.2024/504},
url = {https://mlanthology.org/ijcai/2024/lin2024ijcai-counterfactual/}
}