Shun Zheng

47 papers A* 11A 2Journal 28Unranked 6
YearRankTypeTitle / Venue / Authors
2026 J jnl
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition.
CoRR
Dongyuan Li, Shun Zheng, Chang Xu, Jiang Bian, Renhe Jiang
2025 J jnl
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains.
CoRR
Jiawen Zhang, Zhenwei Zhang, Shun Zheng, Xumeng Wen, Jia Li, Jiang Bian
2025 J jnl
Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning.
Nat. Mac. Intell.
Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian
2025 J jnl
Chemistry-aware battery degradation prediction under simulated real-world cyclic protocols.
CoRR
Yuqi Li, Han Zhang, Xiaofan Gui, Zhao Chen, Yu Li, Xiwen Chi, Quan Zhou, Shun Zheng, Ziheng Lu, Wei Xu, Jiang Bian, Liquan Chen, Hong Li
2025 J jnl
Deep Self-Evolving Reasoning.
CoRR
Zihan Liu, Shun Zheng, Xumeng Wen, Yang Wang, Jiang Bian, Mao Yang
2025 — conf
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting.
KDD (1)
Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie, Kun Yi, Qi Zhang, Jiang Bian, Yanjie Fu
2025 J jnl
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs.
CoRR
Xumeng Wen, Zihan Liu, Shun Zheng, Zhijian Xu, Shengyu Ye, Zhirong Wu, Xiao Liang, Yang Wang, Junjie Li, Ziming Miao, Jiang Bian, Mao Yang
2025 J jnl
Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models.
CoRR
Xumeng Wen, Shun Zheng, Zhen Xu, Yiming Sun, Jiang Bian
2024 J jnl
Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows.
CoRR
Wei Fan, Shun Zheng, Pengyang Wang, Rui Xie, Jiang Bian, Yanjie Fu
2024 A* conf
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation.
ICLR
Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian
2024 J jnl
Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
PLoS Comput. Biol.
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 A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. 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
2024 J jnl
DEWP: Deep Expansion Learning for Wind Power Forecasting.
CoRR
Wei Fan, Yanjie Fu, Shun Zheng, Jiang Bian, Yuanchun Zhou, Hui Xiong
2024 J jnl
DEWP: Deep Expansion Learning for Wind Power Forecasting.
ACM Trans. Knowl. Discov. Data
Wei Fan, Yanjie Fu, Shun Zheng, Jiang Bian, Yuanchun Zhou, Hui Xiong
2024 A* conf
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer.
NeurIPS
Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li
2024 J jnl
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer.
CoRR
Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li
2024 A* conf
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models.
KDD
Xumeng Wen, Han Zhang, Shun Zheng, Wei Xu, Jiang Bian
2024 J jnl
Large Language Model as a Universal Clinical Multi-task Decoder.
CoRR
Yujiang Wu, Hongjian Song, Jiawen Zhang, Xumeng Wen, Shun Zheng, Jiang Bian
2024 A* conf
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration.
ICLR
Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo, Yi Chang
2024 J jnl
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration.
CoRR
Hangting Ye, Wei Fan, Xiaozhuang Song, Shun Zheng, He Zhao, Dandan Guo, Yi Chang
2024 A* conf
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons.
NeurIPS
Jiawen Zhang, Xumeng Wen, Zhenwei Zhang, Shun Zheng, Jia Li, Jiang Bian
2023 J jnl
BatteryML: An Open-source platform for Machine Learning on Battery Degradation.
CoRR
Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian
2023 J jnl
Learning Intra- and Inter-Cell Differences for Accurate Battery Lifespan Prediction across Diverse Conditions.
CoRR
Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian
2023 J jnl
ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting.
CoRR
Jiawen Zhang, Xumeng Wen, Shun Zheng, Jia Li, Jiang Bian
2023 J jnl
Towards Foundation Models for Learning on Tabular Data.
CoRR
Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, Jiang Bian
2023 A* conf
UADB: Unsupervised Anomaly Detection Booster.
ICDE
Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian
2023 J jnl
UADB: Unsupervised Anomaly Detection Booster.
CoRR
Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian
2023 A* conf
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series.
KDD
Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li
2023 J jnl
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series.
CoRR
Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li
2022 A conf
A Graph-based Spatiotemporal Model for Energy Markets.
CIKM
Swati Sharma, Srinivasan Iyengar, Shun Zheng, Kshitij Kapoor, Wei Cao, Jiang Bian, Shivkumar Kalyanaraman, John Lemmon
2022 — conf
A continuous glucose monitoring measurements forecasting approach via sporadic blood glucose monitoring.
BIBM
Yuting Xing, Hangting Ye, Xiaoyu Zhang, Wei Cao, Shun Zheng, Jiang Bian, Yike Guo
2022 A* conf
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting.
ICLR
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
2022 J jnl
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting.
CoRR
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
2022 A* conf
Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations.
NeurIPS
Xiaozhuang Song, Shun Zheng, Wei Cao, James J. Q. Yu, Jiang Bian
2022 A* conf
Learning Differential Operators for Interpretable Time Series Modeling.
KDD
Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian
2022 J jnl
Learning Differential Operators for Interpretable Time Series Modeling.
CoRR
Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian
2022 J jnl
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures.
CoRR
Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, Jian Li
2021 A conf
HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting.
CIKM
Shun Zheng, Zhifeng Gao, Wei Cao, Jiang Bian, Tie-Yan Liu
2021 — conf
Revisiting the Evaluation of End-to-end Event Extraction.
ACL/IJCNLP (Findings)
Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
2021 — conf
The CityLearn Challenge 2022: Overview, Results, and Lessons Learned.
NeurIPS (Competition and Demos)
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
2020 A* conf
SEEK: Segmented Embedding of Knowledge Graphs.
ACL
Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu
2020 J jnl
SEEK: Segmented Embedding of Knowledge Graphs.
CoRR
Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu
2019 — conf
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction.
ACL (1)
Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu
2019 — conf
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction.
EMNLP/IJCNLP (1)
Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
2019 J jnl
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction.
CoRR
Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
2018 J jnl
DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction.
CoRR
Shun Zheng, Peilin Yu, Lu Chen, Ling Huang, Wei Xu
2017 J jnl
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization.
J. Mach. Learn. Res.
Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang
2016 J jnl
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization.
CoRR
Shun Zheng, Fen Xia, Wei Xu, Tong Zhang