Publications

Conference

• DeepHalo: A Neural Choice Model with Controllable Context Effects.
  S. Zhang, Z. Wang, R. Gao and S. Li.
  Neural Information Processing Systems (NeurIPS), 2025. (Spotlight, acceptance rate: 3%)

• RKHS Choice Model.
  Y. Yang, Z. Wang, R. Gao and S. Li.
  ACM Conference on Economics and Computation (EC), 2025.

• Flow-Based Delayed Hawkes Process.
  C. Yang, W. Ren and S. Li.
  Conference on Uncertainty in Artificial Intelligence (UAI), 2025.

• Evolving Minds: Logic-Informed Inference from Temporal Action Patterns.
  C. Yang, S. Cui, Y. Yang and S. Li.
  International Conference on Machine Learning (ICML), 2025.

• Convergence of Mean-Field Langevin Stochastic Descent-Ascent for Distributional Minimax Optimization.
  Z. Liu, F. Liu, R. Gao and S. Li.
  International Conference on Machine Learning (ICML), 2025. (Spotlight, acceptance rate: 2.6%)

• Logic-Logit: A Logic-Based Approach to Choice Modeling.
  S. Zhang, W. Ren and S. Li.
  International Conference on Learning Representations (ICLR), 2025.

• HyperLogic: Enhancing Diversity and Accuracy in Rule Learning with HyperNets.
  Y. Yang, W. Ren and S. Li.
  Neural Information Processing Systems (NeurIPS), 2024.

• Neuro-Symbolic Temporal Point Processes.
  Y. Yang, C. Yang, B. Li, Y. Fu and S. Li.
  International Conference on Machine Learning (ICML), 2024.

• Latent Logic Tree Extraction for Event Sequence Explanation from LLMs.
  Z. Song, C. Yang, C. Wang, B. An and S. Li.
  International Conference on Machine Learning (ICML), 2024.

• Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation.
  Y. Kuang, C. Yang, Y. Yang and S. Li.
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• Enhancing Human-AI Collaboration Through Logic-Guided Reasoning.
  C. Cao, Y. Fu, S. Xu, R. Zhang and S. Li.
  International Conference on Learning Representations (ICLR), 2024.

• Amortized Network Intervention to Steer Excitatory Point Processes.
  Z. Song, W. Ren and S. Li.
  International Conference on Learning Representations (ICLR), 2024.

• Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions.
  C. Cao, C. Yang, R. Zhang and S. Li.
  Neural Information Processing Systems (NeurIPS), 2023.

• Explaining Point Processes by Learning Interpretable Temporal Logic Rules.
  S. Li, M. Feng, L. Wang, A. Essofi, Y. Cao, J. Yan and L. Song.
  International Conference on Learning Representations (ICLR), 2022.

• Temporal Logic Point Processes.
  S. Li, L. Wang, R. Zhang, X. Chang, X. Liu, Y. Xie, Y. Qi, and L. Song.
  International Conference on Machine Learning (ICML), 2020.

• Continuous-Time Dynamic Graph Learning via Neural Interaction Processes.
  X. Chang, X. Liu, J. Wen, S. Li, Y. Fang, L. Song, and Y. Qi.
  The Conference on Information and Knowledge Management (CIKM), 2020.

• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System.
  X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song.
  International Conference on Machine Learning (ICML), 2019.

• Learning Temporal Point Processes via Reinforcement Learning.
  S. Li, S. Xiao, S. Zhu, N. Du, Y. Xie, and L. Song.
  Neural Information Processing Systems (NeurIPS), 2018. (Spotlight, acceptance rate: 168/4856 = 3.5%)

• Recurrent Hidden Semi-Markov Model.
  H. Dai, B. Dai, Y. Zhang, S. Li, and L. Song.
  International Conference on Learning Representations (ICLR), 2017.

• Fake News Mitigation via Point Processes Based Intervention.
  M. Farajtabar, J. Yang, X. Ye, R. Trivedi, E. Khalil, S. Li, H. Xu, L. Song, and H. Zha.
  International Conference on Machine Learning (ICML), 2017.

• Online Seismic Event Picking Via Sequential Change-Point Detection.
  S. Li, Y. Cao, C. Leamon, Y. Xie, L. Shi, and W. Song.
  Allerton Conference on Control, Communications and Computing (Allerton), 2016.

• M-Statistic for Kernel Change-Point Detection.
  S. Li, Y. Xie, H. Dai, and L. Song.
  Neural Information Processing Systems (NIPS), 2015.

• Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression.
  Y. Liu, S. Li, F. Li, L. Song, and J. Rehg.
  Neural Information Processing Systems (NIPS), 2015.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution.
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song.
  Neural Information Processing Systems (NIPS), 2015. (Oral, acceptance rate: 15/1838 = 0.8%)

Workshop

• Unanchoring the Mind: DAE-Guided Counterfactual Reasoning for Rare Disease Diagnosis.
  Y. Yan, Y. Fu, W. Ren and S. Li.
  NeurIPS Workshop on GenAI4Health, 2025. (Best Paper Award)

• Neural Decision Rule for Constrained Contextual Stochastic Optimization.
  Z. Liu, Z. Xu, F. Liu, R. Gao and S. Li.
  NeurIPS Workshop on MLxOR, 2025. (Spotlight)

• From Counts to Choice: Choice-Driven Spatial-Temporal Counting Process Models.
  C. Yang, Y. Kuang and S. Li.
  NeurIPS Workshop on UrbanAI: Harnessing Artificial Intelligence for Smart Cities, 2025.

• Who Should Be Consulted? Targeted Expert Selection for Rare Disease Diagnosis.
  Y. Fu, C. Yang, X. Chen, Y. Yan and S. Li.
  ICML Workshop on Collaborative and Federated Agentic Workflows, 2025. (Oral)

• Inferring the Invisible: Neuro-Symbolic Rule Discovery for Missing Value Imputation.
  W. Ren, K. Wan, J. Leng and S. Li.
  ICML Workshop on DataWorld: Unifying Data Curation Frameworks Across Domains, 2025.

• Deep Context-Dependent Choice Model.
  S. Zhang, Z. Wang, R. Gao and S. Li.
  ICML Workshop on Models of Human Feedback for AI Alignment, 2025. (Oral)

• Discovering Logic-Informed Intrinsic Rewards to Explain Human Policies.
  C. Cao, Y. Fu, C. Yang and S. Li.
  ICML Workshop on Programmatic Representations for Agent Learning, 2025.

• Counterfactual Optimization of Treatment Policies Based on Temporal Point Processes.
  Z. Jing, C. Yang and S. Li.
  ICML Workshop on Interpretable Machine Learning in Healthcare, 2023.

• Reinforcement Temporal Logic Rule Learning to Explain the Generating Processes of Events.
  C. Yang, L. Wang, Z. Mou and S. Li.
  ICML Workshop on Interpretable Machine Learning in Healthcare, 2022.

• Interpretable Deep Generative Spatio-Temporal Point Processes.
  S. Zhu, S. Li, Z. Peng, and Y. Xie.
  NeurIPS Workshop on AI for Earth Sciences, 2020.

• Temporal Logic Point Processes Processes.
  S. Li, L. Wang, R. Zhang, Y. Xie, N. Du, and L. Song.
  NeurIPS Workshop on Learning with Temporal Point Processes, 2019. (Oral)

• Co-evolutionary Dynamics of Information Diffusion and Network Structure.
  M. Farajtabar, M. Gomez-Rodriguez, Y. Wang, S. Li, H. Zha, and L. Song.
  Workshop on Activity and Events in Networks: Models, Methods Applications, in conjunction with WWW, 2015.

Journal

• Micro-Randomized Trials for Promoting Engagement in Mobile Health Data Collection: Adolescent/Young Adult Oral Chemotherapy Adherence as an Example.
  S. Li, A. Psihogios, E. McKelvey, A. Ahmed, M. Rabbi, and S. Murphy.
  Current Opinion in Systems Biology, 2020.

• Scan B-statistic for Kernel Change-point Detection.
  S. Li, Y. Xie, H. Dai, and L. Song.
  Sequential Analysis, 38(4):503–544, 2019.

  • Finalist of 2018 INFORMS QSR Best Student Paper Competition

• Detecting Weak Changes in Dynamic Events over Networks.
  S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song.
  IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, No. 2, June 2017.

  • Finalist of 2018 INFORMS Social Media Analytics Best Student Paper Competition

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution.
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song.
  Journal of Machine Learning Research, 2017.

• Control for Time-Varying Delay Systems by Integrating Semi-Discretization and Hysteresis-Based Switching.
  C. Shao, S. Li, H. Li, and J. Sheng.
  Asian Journal of Control, 2018.

• COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution.
  M. Farajtabar, Y. Wang, M. Gomez-Rodriguez, S. Li, H. Zha, and L. Song.
  The Web Conference, Journal Track, 2018.

• Reinforcement Learning of Spatio-Temporal Point Processes.
  S. Zhu, S. Li, Z. Peng, and Y. Xie.
  IEEE Transactions on Knowledge and Data Engineering, 2022.

Book Chapter

• Learning Continuous-Time Hidden Markov Models for Event Data.
  Y. Liu, A. Moreno, S. Li, F. Li, L. Song, and J. Rehg.
  In Mobile Health, Springer, 2017