FairSTG: Countering performance heterogeneity via collaborative sample-level optimization

no code implementations • 19 Mar 2024 • Gengyu Lin, Zhengyang Zhou, Qihe Huang, Kuo Yang, Shifen Cheng, Yang Wang

To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up.

Fairness Graph Learning +1

Spatio-Temporal Field Neural Networks for Air Quality Inference

no code implementations • 2 Mar 2024 • Yutong Feng, Qiongyan Wang, Yutong Xia, Junlin Huang, Siru Zhong, Kun Wang, Shifen Cheng, Yuxuan Liang

The air quality inference problem aims to utilize historical data from a limited number of observation sites to infer the air quality index at an unknown location.

Air Quality Inference