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Learning Differential Operators for Interpretable Time Series Modeling


Sep 03, 2022
Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian

* Published in ACM SIGKDD 2022 

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Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble


May 19, 2022
Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

* Accepted in IJCAI 2022. The codes are available at https://seqml.github.io/eppo 

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DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation


Jan 11, 2022
Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

* Accepted by AAAI'22 

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SHGNN: Structure-Aware Heterogeneous Graph Neural Network


Dec 14, 2021
Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu


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KGE-CL: Contrastive Learning of Knowledge Graph Embeddings


Dec 09, 2021
Wentao Xu, Zhiping Luo, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu


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HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information


Oct 26, 2021
Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu


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Instance-wise Graph-based Framework for Multivariate Time Series Forecasting


Sep 14, 2021
Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu


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Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation


Jul 12, 2021
Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian


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Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport


Jun 25, 2021
Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

* Accepted by KDD 2021 (research track) 

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