Publications

Selected Topic: Time Series Foundation Model:

1. TEMPO: Prompt-based generative pre-trained transformer for time series forecasting
   Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu
   ICLR 2024 | paper | code | huggingface

2. TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
   Defu Cao, Wen Ye, Yan Liu
   ICML 2024 Workshop | paper

3. GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting
   Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, Yan Liu
   AAAI/EAAI 2024 | paper | Multimodal Dataset

4. Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution
   Wen Ye, Yizhou Zhang, Wei Yang, Lumingyuan Tang, Defu Cao, Jie Cai, Yan Liu
   Arxiv 2024 | paper

Survey Papers:

1. When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
   Chuizheng Meng, Sungyong Seo, Defu Cao, Sam Griesemer, Yan Liu
   Arxiv 2022 | paper

Conference Papers:

1. “Someone Hid It!”: Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
   Jiate Li, Defu Cao, Li Li, Wei Yang, Yuehan Qin, Chenxiao Yu, Tiannuo Yang, Ryan A. Rossi, Yan Liu, Xiyang Hu, Yue Zhao
   ICML 2026

2. Position: Beyond Prediction: Toward Verifiable Physiological Waveform Reasoning with Foundation Models and Agentic LLMs
   Xiaoda Wang, Ching Chang, Defu Cao, Kaiqiao Han, Fang Sun, Yue Huang, Minxiao Wang, Chang Xu, Xiao Luo, Runze Yan, Xiangliang Zhang, Xiao Hu, Yan Liu, Yizhou Sun, Wei Wang, Carl Yang
   ICML 2026

3. Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting
   Defu Cao, Zijie Lei, Muyan Weng, Jiao Sun, Yan Liu
   IJCAI 2026

4. PINFDiT: Energy-Based Physics-Informed Diffusion Transformers for General-purpose Time Series Tasks
   Defu Cao, Wen Ye, Yizhou Zhang, Sam Griesemer, Yan Liu
   ICLR 2026

5. Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
   Wei Yang, Defu Cao, Jiacheng Pang, Muyan Weng, Yan Liu
   ICLR 2026

6. Orthogonalized estimation of difference of q-functions
   Defu Cao, Angela Zhou
   AISTATS 2026

7. Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
   Xiongye Xiao, Heng Ping, Chenyu Zhou, Defu Cao, Yaxing Li, Yizhuo Zhou, Shixuan Li, Nikos Kanakaris, Paul Bogdan
   ICLR 2025

8. When Splitting Makes Stronger: A Theoretical and Empirical Analysis of Divide-and-Conquer Prompting in LLMs
   Yizhou Zhang*, Defu Cao*, Yan Liu
   CoLM 2025

9. Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
   Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio, Yan Liu
   NeurIPS 2024

10. An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
    Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu
    ICML 2024

11. Neuro-Inspired Hierarchical Multimodal Learning
    Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao, Shixuan Li, Yaxing Li, Tianqing Fang, Mingxi Cheng, Paul Bogdan
    ICLR 2024

12. MUGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
    Tianjun Yao, Jiaqi Sun, Defu Cao, Kun Zhang, Guangyi Chen
    WWW 2024 | paper

13. Large Scale Financial Time Series Forecasting with Multi-Faceted Model
    Defu Cao, Yixiang Zheng, Parisa Hassanzadeh, Simran Lamba, Xiaomo Liu, Yan Liu
    ICAIF 2023 | Oral | paper

14. SVGformer: Representation Learning for Continuous Vector Graphics using Transformers
    Defu Cao, Zhaowen Wang, Jose Echevarria, Yan Liu
    CVPR 2023 | paper

15. Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
    Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan
    ICLR 2024 | paper

16. Time-delayed Multivariate Time Series Predictions
    Hao Niu, Guillaume Habault, Roberto Legaspi, Chuizheng Meng, Defu Cao, Shinya Wada, Chihiro Ono, Yan Liu
    SDM 2023

17. Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
    Defu Cao, James Enouen, Yujing Wang, Xiangchen Song, Chuizheng Meng, Hao Niu, Yan Liu
    AAAI 2023 | paper

18. Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
    Yizhou Zhang, Defu Cao, Yan Liu
    NeurIPS 2022 | paper | code

19. Mu2ReST: Multi-Resolution Recursive Spatio-Temporal Transformer for Long-Term Prediction
    Hao Niu, Chuizheng Meng, Defu Cao, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu
    PAKDD 2022 | paper

20. Enhancing Self-Attention with Knowledge-Assisted Attention Maps
    Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Wei Shen, Defu Cao, Mingliang Zhang, Yaming Yang, Jing Bai, Yunhai Tong, Hao Sun, Ruofei Zhang
    NAACL 2022 | paper

21. Spectral Temporal Graph Neural Network for Trajectory Prediction
    Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
    ICRA 2021 | paper

22. Spectral temporal graph neural network for multivariate time-series forecasting
    Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
    NeurIPS 2020 | paper | code | video | Spotlight (2%)

23. Multivariate time-series anomaly detection via graph attention network
    Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
    ICDM 2020 | paper | code

Workshop Papers:

1. TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
   Defu Cao, Wen Ye, Yan Liu
   ICML 2024 Workshop on Foundation Models in the Wild | paper

2. DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
   Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko, Yan Liu
   NeurIPS 2022 DistShift Workshop | paper | Proposed Dataset

3. Estimating Treatment Effects in Continuous Time with Hidden Confounders
   Defu Cao, James Enouen, Yan Liu
   ICML 2022 | video | paper

Patents:

1. Representation Learning for Continuous Vector Graphics
   Defu Cao, Zhaowen Wang, Jose Echevarria US Patent Application US20240303870A1 | Status: Published Patent Application, United States, 2024 | USPTO

Publication Impact