Selected Publications

Xiaoda Wang, Minxiao Wang, Kaiqiao Han, Defu Cao, Ching Chang, Yidan Shi, Runze Yan, Xiao Luo, Yan Liu, Xiao Hu, Yizhou Sun, Wei Wang, Carl Yang (2026). PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation. arXiv.

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Wen Ye, Wei Yang, Defu Cao, Yizhou Zhang, Lumingyuan Tang, Jie Cai, Yan Liu (2026). TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis. Transactions on Machine Learning Research.

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Wei Yang, Defu Cao, Jiacheng Pang, Muyan Weng (翁牧言), Yan Liu (2026). Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning. The Fourteenth International Conference on Learning Representations.

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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 (2026). Position: Beyond Prediction: Toward Verifiable Physiological Waveform Reasoning with Foundation Models and Agentic LLMs. Proceedings of the 43rd International Conference on Machine Learning (ICML).

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Muyan Weng (翁牧言), Defu Cao, Wei Yang, Yashaswi Sharma, Yan Liu (2026). TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks. arXiv.

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Chuizheng Meng, Jianke Yang, Hao Niu, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu (2026). Sample-Level Prototypical Federated Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence.

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Jiate Li, Defu Cao, Li Li, Wei Yang, Yuehan Qin, Chenxiao Yu, Tiannuo Yang, Ryan A. Rossi, Yan Liu, Xiyang Hu, Yue Zhao (2026). "Someone Hid It!": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval. Proceedings of the 43rd International Conference on Machine Learning (ICML).

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Defu Cao, Wen Ye, Yizhou Zhang, Sam Griesemer, Yan Liu (2026). PINFDiT: Energy-Based Physics-Informed Diffusion Transformers for General-purpose Time Series Tasks. The Fourteenth International Conference on Learning Representations.

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Defu Cao, Michael Gee, Jinbo Liu, Hengxuan Wang, Wei Yang, Rui Wang, Yan Liu (2025). Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting. arXiv.

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Jinbo Liu, Defu Cao, Yifei Wei, Tianyao Su, Yuan Liang, Yushun Dong, Yan Liu, Yue Zhao, Xiyang Hu (2025). Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs. Annual Meeting of the Association for Computational Linguistics (ACL).

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Wei Yang, Muyan Weng (翁牧言), Jiacheng Pang, Defu Cao, Heng Ping, Peiyu Zhang, Shixuan Li, Yue Zhao, Qiang Yang, Mengdi Wang, Yan Liu (2025). Toward Evolutionary Intelligence: LLM-based Agentic Systems with Multi-Agent Reinforcement Learning. SSRN.

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Yuhao Xu, Xiaoda Wang, Jiaying Lu, Sirui Ding, Defu Cao, Huaxiu Yao, Yan Liu, Xiao Hu, Carl Yang (2025). EnECG: Efficient Ensemble Learning for Electrocardiogram Multi-task Foundation Models. Proceedings of the American Medical Informatics Association 2025 Annual Symposium (AMIA).

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Huy Quang Ung, Guillaume Habault, Yasutaka Nishimura, Hao Niu, Roberto Legaspi, Tomoki Oya, Ryoichi Kojima, Masato Taya, Chihiro Ono, Atsunori Minamikawa, Yan Liu (2025). CartoMapQA: A Fundamental Benchmark Dataset Evaluating Vision-Language Models on Cartographic Map Understanding. Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems.

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Ching Chang, Yidan Shi, Defu Cao, Wei Yang, Jeehyun Hwang, Haixin Wang, Jiacheng Pang, Wei Wang, Yan Liu, Wen-Chih Peng, Tien-Fu Chen (2025). A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models. arXiv.

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Wen Ye, Jinbo Liu, Defu Cao, Wei Yang, Yan Liu (2025). When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference. arXiv.

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Wei Yang, Defu Cao, Yan Liu (2025). Foundation Models for Demand Forecasting via Dual-Strategy Ensembling. KDD 2025 Workshop on AI for Supply Chain: Today and Future.

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Zijun Cui (崔梓筠), Sam Griesemer, Sungyong Seo, Joshua Hikida, Yan Liu (2025). Physics-Aware Spatiotemporal Causal Graph Network for Forecasting with Limited Data. Transactions on Machine Learning Research.

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Yizhou Zhang, Defu Cao, Lun Du, Qiang Fu, Yan Liu (2025). When Splitting Makes Stronger: A Theoretical and Empirical Analysis of Divide-and-Conquer Prompting in LLMs. Second Conference on Language Modeling.

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Defu Cao, Zijie Lei, Jiao Sun, Yan Liu (2025). Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting. 1st ICML Workshop on Foundation Models for Structured Data.

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Chuizheng Meng, Sam Griesemer, Defu Cao, Sungyong Seo, Yan Liu (2025). When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning. Machine Learning for Computational Science and Engineering.

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James Enouen, Yan Liu (2025). InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly. The Thirteenth International Conference on Learning Representations.

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Can Rong, Jingtao Ding, Yan Liu, Yong Li (2025). A Large-scale Dataset and Benchmark for Commuting Origin-Destination Flow Generation. The Thirteenth International Conference on Learning Representations.

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Hao Niu, Guillaume Habault, Defu Cao, Yizhou Zhang, Roberto Legaspi, Huy Quang Ung, James Enouen, Shinya Wada, Chihiro Ono, Atsunori Minamikawa, Yan Liu (2024). Mixture of Projection Experts for Multivariate Long-Term Time Series Forecasting. 2024 International Conference on Machine Learning and Applications (ICMLA).

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Sam Griesemer, Defu Cao, Zijun Cui (崔梓筠), Carolina Osorio, Yan Liu (2024). Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference. Advances in Neural Information Processing Systems (NeurIPS).

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James Enouen, Hootan Nakhost, Sayna Ebrahimi, Sercan O. Arik, Yan Liu, Tomas Pfister (2024). TextGenSHAP: Scalable Post-Hoc Explanations in Text Generation with Long Documents. Findings of the Association for Computational Linguistics: ACL 2024.

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

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Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu (2024). An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. Proceedings of the 41st International Conference on Machine Learning.

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Yizhou Zhang, Karishma Sharma, Lun Du, Yan Liu (2024). Toward Mitigating Misinformation and Social Media Manipulation in LLM Era. Companion Proceedings of the ACM Web Conference 2024.

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Defu Cao, Furong Jia, Sercan O. Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu (2024). TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. International Conference on Learning Representations (ICLR).

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Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, Yan Liu (2024). GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

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Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao (2024). TrustLLM: Trustworthiness in Large Language Models. arXiv.

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Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, Yan Liu (2023). Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning. Advances in Neural Information Processing Systems.

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James Enouen, Yan Liu (2023). Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection. arXiv.

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Defu Cao, Yixiang Zheng, Parisa Hassanzadeh, Simran Lamba, Xiaomo Liu, Yan Liu (2023). Large Scale Financial Time Series Forecasting with Multi-faceted Model. 4th ACM International Conference on AI in Finance.

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Defu Cao, Zhaowen Wang, Jose Echevarria, Yan Liu (2023). SVGformer: Representation Learning for Continuous Vector Graphics using Transformers. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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Defu Cao, James Enouen, Yan Liu (2023). Estimating Treatment Effects in Continuous Time with Hidden Confounders. arXiv.

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Emily Nguyen, Zijun Cui (崔梓筠), Georgia Kokaraki, Joseph Carlson, Yan Liu (2023). Transferable and Interpretable Treatment Effectiveness Prediction for Ovarian Cancer via Multimodal Deep Learning. AMIA … Annual Symposium proceedings. AMIA Symposium.

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Hao Niu, Guillaume Habault, Roberto Legaspi, Chuizheng Meng, Defu Cao, Shinya Wada, Chihiro Ono, Yan Liu (2023). Time-delayed Multivariate Time Series Predictions. Proceedings of the 2023 SIAM International Conference on Data Mining (SDM).

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Loc Trinh, Tim Chu, Zijun Cui (崔梓筠), Anand Malpani, Cherine Yang, Istabraq Dalieh, Alvin Hui, Oscar Gomez, Yan Liu, Andrew Hung (2023). Self-supervised Sim-to-Real Kinematics Reconstruction for Video-Based Assessment of Intraoperative Suturing Skills. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023.

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Yizhou Zhang, Karishma Sharma, Yan Liu (2023). Capturing Cross-Platform Interaction for Identifying Coordinated Accounts of Misinformation Campaigns. Advances in Information Retrieval.

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Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko, Yan Liu (2022). DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift. arXiv.

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Yizhou Zhang, Defu Cao, Yan Liu (2022). Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media. arXiv.

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Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen (2022). Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning.

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Chuizheng Meng, Hao Niu, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu (2022). Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data.

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Hao Niu, Chuizheng Meng, Defu Cao, Guillaume Habault, Roberto Legaspi, Shinya Wada, Chihiro Ono, Yan Liu (2022). Mu2ReST: Multi-resolution Recursive Spatio-Temporal Transformer for Long-Term Prediction. Advances in Knowledge Discovery and Data Mining: 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16–19, 2022, Proceedings, Part I.

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Andrew J. Hung, Sirisha Rambhatla, Daniel I. Sanford, Nilay Pachauri, Erik Vanstrum, Jessica H. Nguyen, Yan Liu (2022). Road to automating robotic suturing skills assessment: Battling mislabeling of the ground truth. Surgery.

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Nan Xu, Nitin Kamra, Yan Liu (2021). Treatment Recommendation with Preference-based Reinforcement Learning. 2021 IEEE International Conference on Big Knowledge (ICBK).

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Nitin Kamra, Yan Liu (2021). Gradient-based optimization for multi-resource spatial coverage problems. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence.

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Yizhou Zhang, Karishma Sharma, Yan Liu (2021). VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media. arXiv.

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Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu (2021). PolSIRD: Modeling Epidemic Spread Under Intervention Policies: Analyzing the First Wave of COVID-19 in the USA. Journal of Healthcare Informatics Research.

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Andrew J. Hung, Sirisha Rambhatla, Daniel I. Sanford, Nilay Pachauri, Jessica H. Nguyen, Yan Liu (2021). PD58-08 AUTOMATING SUTURING SKILLS ASSESSMENT WITH A LIMITED SURGEON DATASET: META LEARNING. The Journal of Urology.

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Chuizheng Meng, Sirisha Rambhatla, Yan Liu (2021). Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. arXiv.

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Karishma Sharma, Yizhou Zhang, Emilio Ferrara, Yan Liu (2021). Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours. arXiv.

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Loc Trinh, Yan Liu (2021). An Examination of Fairness of AI Models for Deepfake Detection. arXiv.

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Chuizheng Meng, Loc Trinh, Nan Xu, Yan Liu (2021). MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset. arXiv.

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Nan Xu, Loc Trinh, Sirisha Rambhatla, Zhen Zeng, Jiahao Chen, Samuel Assefa, Yan Liu (2021). SIMULATING CONTINUOUS-TIME HUMAN MOBILITY TRAJECTORIES.

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Sirisha Rambhatla, Sepanta Zeighami, Kameron Shahabi, Cyrus Shahabi, Yan Liu (2020). Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data. arXiv.

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Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu (2020). Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning.

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Nitin Kamra, Hao Zhu, Dweep Trivedi, Ming Zhang, Yan Liu (2020). Multi-agent Trajectory Prediction with Fuzzy Query Attention. arXiv.

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Karishma Sharma, Xinran He, Sungyong Seo, Yan Liu (2020). Network Inference from a Mixture of Diffusion Models for Fake News Mitigation. arXiv.

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Michael Tsang, Sirisha Rambhatla, Yan Liu (2020). How does this interaction affect me? Interpretable attribution for feature interactions. arXiv.

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Hongzhi Shi, Quanming Yao, Qi Guo, YaGuang Li (李亚光), Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu (2020). Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. 2020 IEEE 36th International Conference on Data Engineering (ICDE).

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Guangyu Li, Bo Jiang, Hao Zhu, Zhengping Che, Yan Liu (2020). Generative Attention Networks for Multi-Agent Behavioral Modeling. Proceedings of the AAAI Conference on Artificial Intelligence.

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Karishma Sharma, Pinar Donmez, Enming Luo, Yan Liu, I. Zeki Yalniz (2020). NoiseRank: Unsupervised Label Noise Reduction with Dependence Models. arXiv.

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Nitin Kamra, Umang Gupta, Kai Wang, Fei Fang, Yan Liu, Milind Tambe (2019). DeepFP for Finding Nash Equilibrium in Continuous Action Spaces. Decision and Game Theory for Security: 10th International Conference, GameSec 2019, Stockholm, Sweden, October 30 – November 1, 2019, Proceedings.

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Max Guangyu Li, Bo Jiang, Zhengping Che, Xuefeng Shi, Mengyao Liu, Yiping Meng, Jieping Ye, Yan Liu (2019). DBUS: Human Driving Behavior Understanding System. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

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Jian Chen, Daphne Remulla, Jessica H. Nguyen, Aastha Dua, Yan Liu, Prokar Dasgupta, Andrew J. Hung (2019). Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU international.

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Sungyong Seo*, Chuizheng Meng*, Yan Liu (2019). Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics.

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Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu (2019). Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection.

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Hanpeng Liu, YaGuang Li (李亚光), Michael Tsang, Yan Liu (2019). CoSTCo: A Neural Tensor Completion Model for Sparse Tensors. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

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Zhengping Che, Guangyu Li, Tracy Li, Bo Jiang, Xuefeng Shi, Xinsheng Zhang, Ying Lu, Guobin Wu, Yan Liu, Jieping Ye (2019). D$textasciicircum2$-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios. arXiv.

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Nitin Kamra, Umang Gupta, Kai Wang, Fei Fang, Yan Liu, Milind Tambe (2019). Deep Fictitious Play for Games with Continuous Action Spaces. Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems.

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Sungyong Seo, Yan Liu (2019). Differentiable Physics-informed Graph Networks. arXiv.

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YaGuang Li (李亚光), Chuizheng Meng, Cyrus Shahabi, Yan Liu (2019). Structure-informed Graph Auto-encoder for RelationalInference and Simulation.

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Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, Yan Liu (2019). Combating Fake News: A Survey on Identification and Mitigation Techniques. arXiv.

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Sungyong Seo, Hau Chan, P. Jeffrey Brantingham, Jorja Leap, Phebe Vayanos, Milind Tambe, Yan Liu (2018). Partially Generative Neural Networks for Gang Crime Classification with Partial Information. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society.

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Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, Yan Liu (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. arXiv.

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YaGuang Li (李亚光), Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, Yan Liu (2018). Multi-task Representation Learning for Travel Time Estimation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

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Zhengping Che, Sanjay Purushotham, Guangyu Li, Bo Jiang, Yan Liu (2018). Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series. Proceedings of the 35th International Conference on Machine Learning.

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Andrew J. Hung, Jian Chen, Zhengping Che, Tanachat Nilanon, Anthony Jarc, Micha Titus, Paul J. Oh, Inderbir S. Gill, Yan Liu (2018). Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes. Journal of Endourology.

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Palash Goyal, Nitin Kamra, Xinran He, Yan Liu (2018). DynGEM: Deep Embedding Method for Dynamic Graphs. arXiv.

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Nitin Kamra, Umang Gupta, Yan Liu (2018). Deep Generative Dual Memory Network for Continual Learning. arXiv.

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Nitin Kamra, Umang Gupta, Fei Fang, Yan Liu, Milind Tambe (2018). Policy Learning for Continuous Space Security Games Using Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence.

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Zhengping Che, Jennifer St. Sauver, Hongfang Liu, Yan Liu (2018). Deep Learning Solutions for Classifying Patients on Opioid Use. AMIA Annual Symposium Proceedings.

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Dehua Cheng, Natali Ruchansky, Yan Liu (2018). Matrix completability analysis via graph k-connectivity. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics.

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Sanjay Purushotham, Zhengping Che, Bo Jiang, Tanachat Nilanon, Yan Liu (2018). Relational Multi-Instance Learning for Concept Annotation from Medical Time Series.

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YaGuang Li (李亚光), Rose Yu, Cyrus Shahabi, Yan Liu (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. arXiv.

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Michael Tsang, Dehua Cheng, Yan Liu (2018). Detecting Statistical Interactions from Neural Network Weights. arXiv.

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Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu (2018). Automatically Inferring Data Quality for Spatiotemporal Forecasting.

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Feng Qian, Chengyue Gong, Karishma Sharma, Yan Liu (2018). Neural User Response Generator: Fake News Detection with Collective User Intelligence.

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Michael Tsang, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, Yan Liu (2018). Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability. Advances in Neural Information Processing Systems.

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Koji Matsuo, Sanjay Purushotham, Aida Moeini, Guangyu Li, Hiroko Machida, Yan Liu, Lynda D. Roman (2017). A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer. American journal of obstetrics and gynecology.

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Natali Ruchansky, Sungyong Seo, Yan Liu (2017). CSI: A Hybrid Deep Model for Fake News Detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.

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Rose Yu, Guangyu Li, Yan Liu (2017). Tensor Regression Meets Gaussian Processes. arXiv.

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Sanjay Purushotham, Chuizheng Meng, Zhengping Che, Yan Liu (2017). Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets. arXiv.

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Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, Yan Liu (2017). Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records. arXiv.

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Sungyong Seo, Jing Huang, Hao Yang, Yan Liu (2017). Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. Proceedings of the Eleventh ACM Conference on Recommender Systems.

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Rose Yu, YaGuang Li (李亚光), Cyrus Shahabi, Ugur Demiryurek, Yan Liu (2017). Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting. Proceedings of the 2017 SIAM International Conference on Data Mining (SDM).

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Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu (2017). Variational Recurrent Adversarial Deep Domain Adaptation.

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Xinran He, Yan Liu (2017). Not Enough Data? Joint Inferring Multiple Diffusion Networks via Network Generation Priors. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining.

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Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu (2017). Interpretable Deep Models for ICU Outcome Prediction. AMIA Annual Symposium Proceedings.

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Zhengping Che, Sanjay Purushotham, David Kale, Wenzhe Li, Mohammad Taha Bahadori, Robinder Khemani, Yan Liu (2017). Time Series Feature Learning with Applications to Health Care. Mobile Health: Sensors, Analytic Methods, and Applications.

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Sungyong Seo, Jing Huang, Hao Yang, Yan Liu (2017). Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network.

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Nitin Kamra, Fei Fang, Debarun Kar, Yan Liu, Milind Tambe (2017). Handling Continuous Space Security Games with Neural Networks.

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Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu (2017). Graph convolutional autoencoder with recurrent neural networks for spatiotemporal forecasting. 7th International workshop on clmate informatics.

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Zhengping Che, Xinran He, Ke Xu, Yan Liu (2017). DECADE: A Deep Metric Learning Model for Multivariate Time Series. Nova Scotia.

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Sungyong Seo, Yan Liu, Arash Mohegh, George Ban-Weiss (2017). Data Quality Network for Spatiotemporal Forecasting.

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Jie Chen, Dehua Cheng, Yan Liu (2016). On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps. arXiv.

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Tanachat Nilanon, Jiayu Yao, Junheng Hao, Sanjay Purushotham, Yan Liu (2016). Normal / abnormal heart sound recordings classification using convolutional neural network. 2016 Computing in Cardiology Conference (CinC).

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Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, Linhong Zhu, Rose Yu, Yan Liu (2016). Latent Space Model for Road Networks to Predict Time-Varying Traffic. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

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Rose Yu, Huida Qiu, Zhen Wen, ChingYung Lin, Yan Liu (2016). A Survey on Social Media Anomaly Detection. SIGKDD Explor. Newsl..

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Yi Chang, Jiliang Tang, Dawei Yin, Makoto Yamada, Yan Liu (2016). Timeline summarization from social media with life cycle models. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.

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Rose Yu, Yan Liu (2016). Learning from Multiway Data: Simple and Efficient Tensor Regression. Proceedings of The 33rd International Conference on Machine Learning.

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Rose Yu, Andrew Gelfand, Suju Rajan, Cyrus Shahabi, Yan Liu (2016). Geographic Segmentation via Latent Poisson Factor Model. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining.

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Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros (2016). SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling. Advances in Neural Information Processing Systems.

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Xinran He, Ke Xu, David Kempe, Yan Liu (2016). Learning Influence Functions from Incomplete Observations. Advances in Neural Information Processing Systems.

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Rose Yu, Xinran He, Yan Liu (2015). GLAD: Group Anomaly Detection in Social Media Analysis. ACM Trans. Knowl. Discov. Data.

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Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu (2015). Functional subspace clustering with application to time series. Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37.

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Xinran He, Theodoros Rekatsinas, James Foulds, Lise Getoor, Yan Liu (2015). HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades. Proceedings of the 32nd International Conference on Machine Learning.

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Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng (2015). Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification. Proceedings of The 28th Conference on Learning Theory.

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Rose Yu, Dehua Cheng, Yan Liu (2015). Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams. Proceedings of the 32nd International Conference on Machine Learning.

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Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng (2015). Spectral Sparsification of Random-Walk Matrix Polynomials. arXiv.

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Dehua Cheng, Xinran He, Yan Liu (2015). Model Selection for Topic Models via Spectral Decomposition. arXiv.

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David Kale, Marjan Ghazvininejad, Anil Ramakrishna, Jingrui He, Yan Liu (2015). Hierarchical active transfer learning: SIAM International Conference on Data Mining 2015, SDM 2015. SIAM International Conference on Data Mining 2015, SDM 2015.

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Yi Chang, Makoto Yamada, Antonio Ortega, Yan Liu (2014). Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery. 2014 IEEE International Conference on Data Mining.

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David C. Kale, Dian Gong, Zhengping Che, Yan Liu, Gerard Medioni, Randall Wetzel, Patrick Ross (2014). An Examination of Multivariate Time Series Hashing with Applications to Health Care. 2014 IEEE International Conference on Data Mining.

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Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, Shang-Hua Teng (2014). Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models. arXiv.

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Dehua Cheng, Yan Liu (2014). Parallel gibbs sampling for hierarchical dirichlet processes via gamma processes equivalence. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Dehua Cheng, Mohammad Taha Bahadori, Yan Liu (2014). FBLG: a simple and effective approach for temporal dependence discovery from time series data. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Jingrui He, Yan Liu, Qiang Yang (2014). Linking Heterogeneous Input Spaces with Pivots for Multi-Task Learning. Proceedings of the 2014 SIAM International Conference on Data Mining (SDM).

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Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu (2014). Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. Advances in Neural Information Processing Systems.

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David C Kale, Zhengping Che, Yan Liu (2014). Computational discovery of physiomes in critically ill children using deep learning.

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Fei Liu, Sounak Chakraborty, Fan Li, Yan Liu, Aurelie C. Lozano (2014). Bayesian regularization via graph Laplacian. Bayesian Analysis.

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Dehua Cheng, Xinran He, Yan Liu (2014). Analyzing the Number of Latent Topics via Spectral Decomposition. stat.

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David Kale, Yan Liu (2013). Accelerating Active Learning with Transfer Learning. 2013 IEEE 13th International Conference on Data Mining.

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Mohammad Taha Bahadori, Yan Liu, Eric P. Xing (2013). Fast structure learning in generalized stochastic processes with latent factors. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Mohammad Taha Bahadori, Yan Liu (2013). An Examination of Practical Granger Causality Inference. Proceedings of the 2013 SIAM International Conference on Data Mining (SDM).

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Jeon-Hyung Kang, Jun Ma, Yan Liu (2013). Transfer Topic Modeling with Ease and Scalability. arXiv.

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Huida Qiu, Yan Liu, Niranjan A. Subrahmanya, Weichang Li (2012). Granger Causality for Time-Series Anomaly Detection. 2012 IEEE 12th International Conference on Data Mining.

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Wenjun Zhou, Hongxia Jin, Yan Liu (2012). Community discovery and profiling with social messages. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Yan Liu, Taha Bahadori, Hongfei Li (2012). Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling. arXiv.

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Sanjay Purushotham, Yan Liu, C.-C. Jay Kuo (2012). Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems. arXiv.

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Mohammad Taha Bahadori, Yan Liu (2012). Granger Causality Analysis in Irregular Time Series. Proceedings of the 2012 SIAM International Conference on Data Mining (SDM).

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Mohammad Taha Bahadori, Yan Liu, Dan Zhang (2011). Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning. Proceedings of the 2011 IEEE 11th International Conference on Data Mining.

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Matheus Hauder, Yolanda Gil, Yan Liu (2011). A Framework for Efficient Data Analytics through Automatic Configuration and Customization of Scientific Workflows. 2011 IEEE Seventh International Conference on eScience.

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Dan Zhang, Yan Liu, Richard Lawrence, Vijil Chenthamarakshan (2011). Transfer Latent Semantic Learning: Microblog Mining with Less Supervision. Proceedings of the AAAI Conference on Artificial Intelligence.

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Dan Zhang, Yan Liu, Luo Si (2011). Serendipitous learning: learning beyond the predefined label space. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Dan Zhang, Jingrui He, Yan Liu, Luo Si, Richard Lawrence (2011). Multi-view transfer learning with a large margin approach. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Yan Liu, Pei-Yun Hseuh, Rick Lawrence, Steve Meliksetian, Claudia Perlich, Alejandro Veen (2011). Latent graphical models for quantifying and predicting patent quality. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Yi Chang, Ruiqiang Zhang, Srihari Reddy, Yan Liu (2011). Detecting Multilingual and Multi-Regional Query Intent in Web Search. Proceedings of the AAAI Conference on Artificial Intelligence.

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Yan Liu, Alexandru Niculescu-Mizil, Aurélie Lozano, Yong Lu (2011). Temporal graphical models for cross-species gene regulatory network discovery. Journal of Bioinformatics and Computational Biology.

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Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard Lawrence (2011). Multiple Instance Learning on Structured Data. Advances in Neural Information Processing Systems.

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Qihua Wang, Hongxia Jin, Yan Liu (2010). Collaboration analytics: mining work patterns from collaboration activities. Proceedings of the 19th ACM international conference on Information and knowledge management.

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Xi Chen, Yan Liu, Han Liu, Jaime Carbonell (2010). Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. Proceedings of the AAAI Conference on Artificial Intelligence.

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Yan Liu, Alexandru Niculescu-Mizil, Aurélie Lozano, Yong Lu (2010). Learning temporal causal graphs for relational time-series analysis. Proceedings of the 27th International Conference on International Conference on Machine Learning.

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Saharon Rosset, Claudia Perlich, Grzergorz Świrszcz, Prem Melville, Yan Liu (2010). Medical data mining: insights from winning two competitions. Data Mining and Knowledge Discovery.

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Yan Liu, Pei-Yun Hsueh, Jennifer Lai, Mirweis Sangin, Marc-Antoine Nussli, Pierre Dillenbourg (2009). Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications. 2009 IEEE International Conference on Multimedia and Expo.

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Yan Liu, Alexandru Niculescu-Mizil, Wojciech Gryc (2009). Topic-link LDA: joint models of topic and author community. Proceedings of the 26th Annual International Conference on Machine Learning.

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Aurelie C. Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan Hosking, Naoki Abe (2009). Spatial-temporal causal modeling for climate change attribution. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Yan Liu, Jayant R. Kalagnanam, Oivind Johnsen (2009). Learning dynamic temporal graphs for oil-production equipment monitoring system. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset (2009). Grouped graphical Granger modeling methods for temporal causal modeling. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe, Yan Liu (2009). Proximity-Based Anomaly Detection using Sparse Structure Learning. Proceedings of the 2009 SIAM International Conference on Data Mining (SDM).

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Jingrui He, Yan Liu, Richard Lawrence (2008). Graph-Based Rare Category Detection. 2008 Eighth IEEE International Conference on Data Mining.

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Claudia Perlich, Prem Melville, Yan Liu, Grzegorz Świrszcz, Richard Lawrence, Saharon Rosset (2008). Breast cancer identification: KDD CUP winner's report. SIGKDD Explor. Newsl..

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Yan Liu, Zhenzhen Kou (2007). Predicting who rated what in large-scale datasets. SIGKDD Explor. Newsl..

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Saharon Rosset, Claudia Perlich, Yan Liu (2007). Making the most of your data: KDD Cup 2007 \"How Many Ratings\" winner's report. SIGKDD Explor. Newsl..

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Andrew Arnold, Yan Liu, Naoki Abe (2007). Temporal causal modeling with graphical granger methods. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Jun Yang, Yan Liu, Eric P. Xing, Alexander G. Hauptmann (2007). Harmonium Models for Semantic Video Representation and Classification. Proceedings of the 2007 SIAM International Conference on Data Mining (SDM).

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Katharina Probst, Rayid Ghani, Marko Krema, Andrew Fano, Yan Liu (2007). Semi-supervised learning of attribute-value pairs from product descriptions. Proceedings of the 20th international joint conference on Artifical intelligence.

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Prem Melville, Yan Liu, Richard Lawrence, Ildar Khabibrakhmanov, Cezar Pendus, Timothy Bowden (2007). Finding New Customers Using Unstructured and Structured Data.

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Rayid Ghani, Katharina Probst, Yan Liu, Marko Krema, Andrew Fano (2006). Text mining for product attribute extraction. SIGKDD Explor. Newsl..

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Yan Liu, Jaime Carbonell, Peter Weigele, Vanathi Gopalakrishnan (2006). Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs). Journal of Computational Biology.

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Yan Liu, Jaime Carbonell, Peter Weigele, Vanathi Gopalakrishnan (2005). Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition. Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology.

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Yan Liu, Eric P Xing, Jaime G. Carbonell (2005). Predicting Protein Folds with Structural Repeats Using a Chain Graph Model.

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Yan Liu, Jaime Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan (2004). Comparison of probabilistic combination methods for protein secondary structure prediction. Bioinformatics (Oxford, England).

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John Lafferty, Xiaojin Zhu, Yan Liu (2004). Kernel conditional random fields: representation and clique selection. Proceedings of the twenty-first international conference on Machine learning.

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Rong Yan, Yan Liu, Rong Jin, A. Hauptmann (2003). On predicting rare classes with SVM ensembles in scene classification. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03)..

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Yan Liu, Jaime Carbonell, Rong Jin (2003). A New Pairwise Ensemble Approach for Text Classification. Machine Learning: ECML 2003.

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Rong Jin, Yan Liu, Luo Si, Jaime G. Carbonell, Alexander G. Hauptmann (2003). A New Boosting Algorithm Using Input-Dependent Regularizer.

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Yan Liu, Jaime G. Carbonell, Vanathi Gopalakrishnan, Peter Wiegele (2002). Protein Quaternary Fold Recognition Using Conditional Graphical Models.

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Yan Liu, Yiming Yang, Jaime Carbonell (2002). Boosting to Correct Inductive Bias in Text Classification.

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