| 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 The Journal of Machine Learning Research 18 (1), 1305-1353, 2017 | 174 | 2017 |
| Shaping social activity by incentivizing users M Farajtabar, N Du, M Gomez Rodriguez, I Valera, H Zha, L Song Advances in neural information processing systems 27, 2474-2482, 2014 | 141 | 2014 |
| Dirichlet-hawkes processes with applications to clustering continuous-time document streams N Du, M Farajtabar, A Ahmed, AJ Smola, L Song Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 131 | 2015 |
| Learning granger causality for hawkes processes H Xu, M Farajtabar, H Zha International conference on machine learning, 1717-1726, 2016 | 122 | 2016 |
| Improved knowledge distillation via teacher assistant: Bridging the gap between student and teacher SI Mirzadeh, M Farajtabar, A Li, H Ghasemzadeh AAAI 2020, 2020 | 100* | 2020 |
| Dyrep: Learning representations over dynamic graphs R Trivedi, M Farajtabar, P Biswal, H Zha International Conference on Learning Representations (ICLR), 2019 | 97* | 2019 |
| More robust doubly robust off-policy evaluation M Farajtabar, Y Chow, M Ghavamzadeh International Conference on Machine Learning (ICML), 1446-1455, 2018 | 94 | 2018 |
| 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 arXiv preprint arXiv:1703.07823, 2017 | 90 | 2017 |
| Wasserstein learning of deep generative point process models S Xiao, M Farajtabar, X Ye, J Yan, L Song, H Zha Advances in neural information processing systems, 3247-3257, 2017 | 86 | 2017 |
| Back to the past: Source identification in diffusion networks from partially observed cascades M Farajtabar, MG Rodriguez, M Zamani, N Du, H Zha, L Song Artificial Intelligence and Statistics, 232-240, 2015 | 78 | 2015 |
| Learning time series associated event sequences with recurrent point process networks S Xiao, J Yan, M Farajtabar, L Song, X Yang, H Zha IEEE transactions on neural networks and learning systems 30 (10), 3124-3136, 2019 | 48* | 2019 |
| Multistage Campaigning in Social Networks M Farajtabar, X Ye, S Harati, L Song, H Zha Advances in Neural Information Processing Systems, 4718-4726, 2016 | 43 | 2016 |
| Adapting auxiliary losses using gradient similarity Y Du, WM Czarnecki, SM Jayakumar, M Farajtabar, R Pascanu, ... arXiv preprint arXiv:1812.02224, 2018 | 42 | 2018 |
| Recurrent poisson factorization for temporal recommendation SA Hosseini, A Khodadadi, K Alizadeh, A Arabzadeh, M Farajtabar, H Zha, ... IEEE Transactions on Knowledge and Data Engineering 32 (1), 121-134, 2018 | 40 | 2018 |
| Correlated cascades: Compete or cooperate A Zarezade, A Khodadadi, M Farajtabar, HR Rabiee, H Zha AAAI 2017, 2017 | 40 | 2017 |
| From local similarity to global coding: An application to image classification A Shaban, HR Rabiee, M Farajtabar, M Ghazvininejad Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 40 | 2013 |
| NetCodec: Community Detection from Individual Activities L Tran, M Farajtabar, L Song, H Zha SIAM International Conference on Data Mining, 2015 | 35 | 2015 |
| Distilling information reliability and source trustworthiness from digital traces B Tabibian, I Valera, M Farajtabar, L Song, B Schölkopf, ... Proceedings of the 26th International Conference on World Wide Web, 847-855, 2017 | 31 | 2017 |
| Smart broadcasting: Do you want to be seen? MR Karimi, E Tavakoli, M Farajtabar, L Song, M Gomez Rodriguez Proceedings of the 22nd ACM SIGKDD international conference on Knowledge …, 2016 | 31 | 2016 |
| Learning Conditional Generative Models for Temporal Point Processes. S Xiao, H Xu, J Yan, M Farajtabar, X Yang, L Song, H Zha AAAI 2018, 2018 | 26 | 2018 |
Hongyuan Zha 査宏远School of Data Science, The Chinese University of Hong Kong, ShenzhenVerified email at cuhk.edu.cn
