| Coevolve: A joint point process model for information diffusion and network evolution M Farajtabar, Y Wang, M Gomez-Rodriguez, S Li, H Zha, L Song Journal of Machine Learning Research 18 (41), 1-49, 2017 | 305 | 2017 |
| Generative adversarial user model for reinforcement learning based recommendation system X Chen, S Li, H Li, S Jiang, Y Qi, L Song International conference on machine learning, 1052-1061, 2019 | 267 | 2019 |
| Fake news mitigation via point process based intervention M Farajtabar, J Yang, X Ye, H Xu, R Trivedi, E Khalil, S Li, L Song, H Zha International conference on machine learning, 1097-1106, 2017 | 220 | 2017 |
| M-statistic for kernel change-point detection S Li, Y Xie, H Dai, L Song Advances in Neural Information Processing Systems 28, 2015 | 191* | 2015 |
| Efficient learning of continuous-time hidden markov models for disease progression YY Liu, S Li, F Li, L Song, JM Rehg Advances in neural information processing systems 28, 2015 | 140 | 2015 |
| Learning temporal point processes via reinforcement learning S Li, S Xiao, S Zhu, N Du, Y Xie, L Song Advances in neural information processing systems 31, 2018 | 130 | 2018 |
| Tcl: Transformer-based dynamic graph modelling via contrastive learning L Wang, X Chang, S Li, Y Chu, H Li, W Zhang, X He, L Song, J Zhou, ... arXiv preprint arXiv:2105.07944, 2021 | 74 | 2021 |
| Continuous-time dynamic graph learning via neural interaction processes X Chang, X Liu, J Wen, S Li, Y Fang, L Song, Y Qi Proceedings of the 29th ACM international conference on information …, 2020 | 67 | 2020 |
| Detecting changes in dynamic events over networks S Li, Y Xie, M Farajtabar, A Verma, L Song IEEE Transactions on Signal and Information Processing over Networks 3 (2 …, 2017 | 54 | 2017 |
| Recurrent hidden semi-markov model H Dai, B Dai, YM Zhang, S Li, L Song International Conference on Learning Representations, 2017 | 37 | 2017 |
| Imitation learning of neural spatio-temporal point processes S Zhu, S Li, Z Peng, Y Xie IEEE Transactions on Knowledge and Data Engineering 34 (11), 5391-5402, 2021 | 29 | 2021 |
| Temporal logic point processes S Li, L Wang, R Zhang, X Chang, X Liu, Y Xie, Y Qi, L Song International Conference on Machine Learning, 5990-6000, 2020 | 25 | 2020 |
| Explaining point processes by learning interpretable temporal logic rules S Li, M Feng, L Wang, A Essofi, Y Cao, J Yan, L Song International Conference on Learning Representations, 2021 | 24 | 2021 |
| Learning continuous-time hidden markov models for event data YY Liu, A Moreno, S Li, F Li, L Song, JM Rehg Mobile Health: Sensors, Analytic Methods, and Applications, 361-387, 2017 | 19 | 2017 |
| Co-evolutionary dynamics of information diffusion and network structure M Farajtabar, M Gomez-Rodriguez, Y Wang, S Li, H Zha, L Song Proceedings of the 24th International Conference on World Wide Web, 619-620, 2015 | 15 | 2015 |
| Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example S Li, AM Psihogios, ER McKelvey, A Ahmed, M Rabbi, S Murphy Current opinion in systems biology 21, 1-8, 2020 | 12 | 2020 |
| Neural model-based reinforcement learning for recommendation X Chen, S Li, H Li, S Jiang, L Song | 9 | 2018 |
| Interpretable deep generative spatio-temporal point processes S Zhu, S Li, Z Peng, Y Xie AI for Earth Sciences Workshop at the 34th Conference on Neural Information …, 2020 | 8 | 2020 |
| Discovering intrinsic spatial-temporal logic rules to explain human actions C Cao, C Yang, R Zhang, S Li Advances in Neural Information Processing Systems 36, 67948-67959, 2023 | 7 | 2023 |
| Understanding the spread of COVID-19 epidemic: A spatio-temporal point process view S Li, L Wang, X Chen, Y Fang, Y Song arXiv preprint arXiv:2106.13097, 2021 | 6 | 2021 |
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