Publications

[TKDE'21] Gas-Theft Suspect Detection among Boiler Room Users: A Data-Driven Approach.

IEEE Transactions on Knowledge and Data Engineering, 2021.

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[WWW'21] Fine-grained Urban Flow Prediction.

In Proceedings of The Web Conference 2021 (WWW'21).

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[WWW'21] AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph.

In Proceedings of The Web Conference 2021 (WWW'21).

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[AAAI'21] Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network.

In Proceedings of the 35th AAAI Conference.

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[IEEE IntSys'20] Federated Digital Gateway: Methodologies, Tools and Applications.

IEEE Intelligent Systems, 2020.

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[CIKM'20] You Are How You Use: Catching Gas Theft Suspects among Diverse Restaurant Users.

In Proceedings of The 29th ACM International Conference on Information and Knowledge Management.

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[TKDE'20] Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks.

IEEE Transactions on Knowledge and Data Engineering, 2020.

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[ECML-PKDD'20] Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics.

In Proceedings of ECML-PKDD 2020.

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[KDD'20] AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction.

In Proceedings of The 26th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, 2020.

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[TKDE'20] Spatio-Temporal Meta Learning for Urban Traffic Prediction.

IEEE Transactions on Knowledge and Data Engineering, 2020.

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[TBD'20] Federated Forest.

IEEE Transactions on Big Data, 2020.

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[InfFus] Predicting and ranking box office revenue of movies based on big data.

Information Fusion, 2020.

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[InfFus] Urban flow prediction from spatiotemporal data using machine learning: A survey.

Information Fusion, 2020.

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[ACM UbiComp'20] CityGuard: Citywide Fire Risk Forecasting Using A Machine Learning Approach.

In Proceedings of The 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing.

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[CIKM'19] Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction.

In Proceedings of The 28th ACM International Conference on Information and Knowledge Management.

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[CIKM'19] CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach.

In Proceedings of The 28th ACM International Conference on Information and Knowledge Management.

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[KDD'19] UrbanFM: Inferring Fine-Grained Urban Flows.

In Proceedings of The 25th ACM SIGKDD international conference on Knowledge Discovery and Data Mining.

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[KDD'19] Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning.

In Proceedings of The 25th ACM SIGKDD international conference on Knowledge Discovery and Data Mining.

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[ACL'19] DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction.

In Proceedings of The 57th Annual Meeting of the Association for Computational Linguistics.

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[TKDE'20] Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning.

IEEE Transactions on Knowledge and Data Engineering, 2020.

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[CIKM'18] DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction.

In Proceedings of The 27th ACM International Conference on Information and Knowledge Management.

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[KDD'18] Deep Distributed Fusion Network for Air Quality Prediction.

In Proceedings of The 24th ACM SIGKDD international conference on Knowledge Discovery and Data Mining.

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[IJCAI'18] GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction.

In Proceedings of The 27th International Joint Conference on Artificial Intelligence.

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[AI'18] Predicting citywide crowd flows using deep spatio-temporal residual networks.

Artificial Intelligence.

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[AI'18] Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks.

Artificial Intelligence, 2018.

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[AAAI'18] When Will You Arrive Estimating Travel Time Based on Deep Neural Networks.

In Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence.

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[AAAI'17] Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction.

In Proceedings of The 31st AAAI Conference on Artificial Intelligence.

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[ACM SIGSPATIAL'16] DNN-Based Prediction Model for Spatial-Temporal Data.

In Proceedings of The 24th ACM International Conference on Advances in Geographical Information Systems.

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[InfSci'16] Efficient parallel boolean matrix based algorithms for computing composite rough set approximations.

Information Sciences, 2016.

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[TKDE'15] A Parallel Matrix-based Method for Computing Approximations in Incomplete Information Systems.

IEEE Transactions on Knowledge and Data Engineering, 2015.

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[KDD'14] Supervised Deep Learning with Auxiliary Networks.

In Proceedings of The 20th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, 2014.

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[InfSci'14] Composite rough sets for dynamic data mining.

Information Sciences, 2014.

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