ST-AutoML

AutoSTG+: An Automatic Framework to Discover the Optimal Network for Spatio-temporal Graph Prediction
AI 2023
Songyu Ke, Zheyi Pan*, Tianfu He, Yuxuan Liang, Junbo Zhang, Yu Zheng
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AutoSTG+: An Automatic Framework to Discover the Optimal Network for Spatio-temporal Graph Prediction
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can …
Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang
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AutoSTL: Automated Spatio-Temporal Multi-Task Learning
EAST: An Enhanced Automated Machine Learning Library for Spatio-Temporal Forecasting
Huge spatio-temporal data (e.g., traffic flow, human mobility, and geographical data) are generated in modern cities. Accurately …
Songyu Ke, Li Song, Kainan Bao, Zheyi Pan*, Junbo Zhang#, Yu Zheng
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EAST: An Enhanced Automated Machine Learning Library for Spatio-Temporal Forecasting
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph
WWW 2021
Zheyi Pan*, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang#, Yu Zheng
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AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph
AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction
[ORAL] KDD 2020, Research Track (acceptance rate: 16.8%)
Ting Li, Junbo Zhang#, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng
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AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction