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2020 – today

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2024
[j4]
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  - journals/ploscb/LopezCPDOAACDLLMXBRGGHJ24
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Velma K. Lopez, Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson:
Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. 20(5): 1011200 (2024)
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Wei Fan, Yanjie Fu, Shun Zheng, Jiang Bian, Yuanchun Zhou, Hui Xiong:
DEWP: Deep Expansion Learning for Wind Power Forecasting. ACM Trans. Knowl. Discov. Data 18(3): 71:1-71:21 (2024)
[c18]
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Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo, Yi Chang:
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration. ICLR 2024
[c17]
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Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian:
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation. ICLR 2024
[c16]
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Xumeng Wen, Han Zhang, Shun Zheng, Wei Xu, Jiang Bian:
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models. KDD 2024: 3323-3333
[i18]
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  - journals/corr/abs-2401-00644
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Wei Fan, Yanjie Fu, Shun Zheng, Jiang Bian, Yuanchun Zhou, Hui Xiong:
DEWP: Deep Expansion Learning for Wind Power Forecasting. CoRR abs/2401.00644 (2024)
[i17]
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  - journals/corr/abs-2401-16777
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Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie, Jiang Bian, Yanjie Fu:
Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows. CoRR abs/2401.16777 (2024)
[i16]
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  - journals/corr/abs-2406-12738
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Yujiang Wu, Hongjian Song, Jiawen Zhang, Xumeng Wen, Shun Zheng, Jiang Bian:
Large Language Model as a Universal Clinical Multi-task Decoder. CoRR abs/2406.12738 (2024)
[i15]
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  - journals/corr/abs-2407-05364
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Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo, Yi Chang:
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration. CoRR abs/2407.05364 (2024)
[i14]
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  - journals/corr/abs-2411-01842
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Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li:
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer. CoRR abs/2411.01842 (2024)
2023
[c15]
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  - conf/adma/LiWZWZS23
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Keke Li, Shaoqing Wang, Shun Zheng, Xia Wu, Yao Zhang, Fuzhen Sun:
Efficient Graph Collaborative Filtering with Multi-layer Output-Enhanced Contrastive Learning. ADMA (1) 2023: 755-771
[c14]
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Shun Zheng, Shaoqing Wang, Lijie Zhang, Yao Zhang, Fuzhen Sun:
Multi-pair Contrastive Learning Based on Same-Timestamp Data Augmentation for Sequential Recommendation. APWeb/WAIM (3) 2023: 174-187
[c13]
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  - conf/icde/Ye0SCZGZC023
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Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian:
UADB: Unsupervised Anomaly Detection Booster. ICDE 2023: 2593-2606
[c12]
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Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li:
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series. KDD 2023: 3273-3285
[i13]
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  - journals/corr/abs-2306-01997
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Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian:
UADB: Unsupervised Anomaly Detection Booster. CoRR abs/2306.01997 (2023)
[i12]
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  - journals/corr/abs-2306-09368
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Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li:
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series. CoRR abs/2306.09368 (2023)
[i11]
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  - journals/corr/abs-2310-05052
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Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian:
Learning Intra- and Inter-Cell Differences for Accurate Battery Lifespan Prediction across Diverse Conditions. CoRR abs/2310.05052 (2023)
[i10]
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  - journals/corr/abs-2310-07338
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Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, Jiang Bian:
Towards Foundation Models for Learning on Tabular Data. CoRR abs/2310.07338 (2023)
[i9]
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  - journals/corr/abs-2310-07446
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Jiawen Zhang, Xumeng Wen, Shun Zheng, Jia Li, Jiang Bian:
ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting. CoRR abs/2310.07446 (2023)
[i8]
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  - journals/corr/abs-2310-14714
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Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian:
BatteryML: An Open-source platform for Machine Learning on Battery Degradation. CoRR abs/2310.14714 (2023)
2022
[c11]
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  - conf/bibm/XingYZCZBG22
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Yuting Xing, Hangting Ye, Xiaoyu Zhang, Wei Cao, Shun Zheng, Jiang Bian, Yike Guo:
A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring. BIBM 2022: 860-863
[c10]
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Swati Sharma, Srinivasan Iyengar, Shun Zheng, Kshitij Kapoor, Wei Cao, Jiang Bian, Shivkumar Kalyanaraman, John Lemmon:
A Graph-based Spatiotemporal Model for Energy Markets. CIKM 2022: 4459-4463
[c9]
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Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu:
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting. ICLR 2022
[c8]
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  - conf/kdd/Luo0LLZ022
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Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian:
Learning Differential Operators for Interpretable Time Series Modeling. KDD 2022: 1192-1201
[c7]
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Xiaozhuang Song, Shun Zheng, Wei Cao, James J. Q. Yu, Jiang Bian:
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations. NeurIPS 2022
[i7]
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  - journals/corr/abs-2203-07681
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Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu:
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting. CoRR abs/2203.07681 (2022)
[i6]
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  - journals/corr/abs-2207-01186
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Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, Jian Li:
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures. CoRR abs/2207.01186 (2022)
[i5]
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  - journals/corr/abs-2209-01491
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Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian:
Learning Differential Operators for Interpretable Time Series Modeling. CoRR abs/2209.01491 (2022)
2021
[c6]
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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Revisiting the Evaluation of End-to-end Event Extraction. ACL/IJCNLP (Findings) 2021: 4609-4617
[c5]
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Shun Zheng, Zhifeng Gao, Wei Cao, Jiang Bian, Tie-Yan Liu:
HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting. CIKM 2021: 4383-4392
[c4]
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  - conf/nips/NweyeNMCSHDHDLJZYZYMKIZMYSZDZB21
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Kingsley Nweye, Zoltán Nagy, Sharada P. Mohanty, Dipam Chakraborty, Siva Sankaranarayanan, Tianzhen Hong, Sourav Dey, Gregor Henze, Ján Drgona, Fangquan Lin, Wei Jiang, Hanwei Zhang, Zhongkai Yi, Jihai Zhang, Cheng Yang, Matthew Motoki, Sorapong Khongnawang, Michael Ibrahim, Abilmansur Zhumabekov, Daniel May, Zhihu Yang, Xiaozhuang Song, Han Zhang, Xiaoning Dong, Shun Zheng, Jiang Bian:
The CityLearn Challenge 2022: Overview, Results, and Lessons Learned. NeurIPS (Competition and Demos) 2021: 85-103
2020
[c3]
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Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu:
SEEK: Segmented Embedding of Knowledge Graphs. ACL 2020: 3888-3897
[i4]
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  - journals/corr/abs-2005-00856
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Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu:
SEEK: Segmented Embedding of Knowledge Graphs. CoRR abs/2005.00856 (2020)

2010 – 2019

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2019
[c2]
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Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu:
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction. ACL (1) 2019: 1419-1429
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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. EMNLP/IJCNLP (1) 2019: 337-346
[i3]
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Shun Zheng, Wei Cao, Wei Xu, Jiang Bian:
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction. CoRR abs/1904.07535 (2019)
2018
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  - journals/corr/abs-1811-02166
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Shun Zheng, Peilin Yu, Lu Chen, Ling Huang, Wei Xu:
DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction. CoRR abs/1811.02166 (2018)
2017
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Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang:
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. J. Mach. Learn. Res. 18: 115:1-115:52 (2017)
2016
[i1]
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Shun Zheng, Fen Xia, Wei Xu, Tong Zhang:
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. CoRR abs/1604.03763 (2016)
2012
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Nam Su Heo, Shun Zheng, Minho Yang, Seok Jae Lee, Sang Yup Lee, Hwa-Jung Kim, Jung Youn Park, Chang-Soo Lee, Taejung Park:
Label-Free Electrochemical Diagnosis of Viral Antigens with Genetically Engineered Fusion Protein. Sensors 12(8): 10097-10108 (2012)

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