Gromov-wasserstein learning for graph matching and node embedding H Xu, D Luo, H Zha, LC Duke International conference on machine learning, 6932-6941, 2019 | 266 | 2019 |
Scalable Gromov-Wasserstein learning for graph partitioning and matching H Xu, D Luo, L Carin Advances in neural information processing systems 32, 2019 | 194 | 2019 |
Learning Hawkes processes from short doubly-censored event sequences H Xu, D Luo, H Zha International Conference on Machine Learning, 3831-3840, 2017 | 80 | 2017 |
Multi-task multi-dimensional hawkes processes for modeling event sequences D Luo, H Xu, Y Zhen, X Ning, H Zha, X Yang, W Zhang ACM, 2015 | 76 | 2015 |
Learning autoencoders with relational regularization H Xu, D Luo, R Henao, S Shah, L Carin International Conference on Machine Learning, 10576-10586, 2020 | 50 | 2020 |
You are what you watch and when you watch: Inferring household structures from IPTV viewing data D Luo, H Xu, H Zha, J Du, R Xie, X Yang, W Zhang IEEE Transactions on Broadcasting 60 (1), 61-72, 2014 | 42 | 2014 |
Benefits from superposed hawkes processes H Xu, D Luo, X Chen, L Carin International Conference on Artificial Intelligence and Statistics, 623-631, 2018 | 25 | 2018 |
Learning graphons via structured gromov-wasserstein barycenters H Xu, D Luo, L Carin, H Zha Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10505 …, 2021 | 23 | 2021 |
Learning mixtures of markov chains from aggregate data with structural constraints D Luo, H Xu, Y Zhen, B Dilkina, H Zha, X Yang, W Zhang IEEE Transactions on Knowledge and Data Engineering 28 (6), 1518-1531, 2016 | 18 | 2016 |
灰色风险型多属性群决策方法 罗党, 周玲, 罗迪新 系统工程与电子技术 30 (9), 1674-1678, 2008 | 17 | 2008 |
Representing graphs via Gromov-Wasserstein factorization H Xu, J Liu, D Luo, L Carin IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (1), 999-1016, 2022 | 14 | 2022 |
Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes. H Xu, D Luo, L Carin IJCAI, 2905-2911, 2018 | 13 | 2018 |
Dictionary learning with mutually reinforcing group-graph structures H Xu, L Yu, D Luo, H Zha, Y Xu Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 7 | 2015 |
Hierarchical optimal transport for robust multi-view learning D Luo, H Xu, L Carin arXiv preprint arXiv:2006.03160, 2020 | 5 | 2020 |
Fused gromov-wasserstein alignment for hawkes processes D Luo, H Xu, L Carin arXiv preprint arXiv:1910.02096, 2019 | 5 | 2019 |
Differentiable hierarchical optimal transport for robust multi-view learning D Luo, H Xu, L Carin IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (6), 7293-7307, 2022 | 4 | 2022 |
Weakly-supervised temporal action alignment driven by unbalanced spectral fused Gromov-Wasserstein distance D Luo, Y Wang, A Yue, H Xu Proceedings of the 30th ACM International Conference on Multimedia, 728-739, 2022 | 4 | 2022 |
Learning graphon autoencoders for generative graph modeling H Xu, P Zhao, J Huang, D Luo arXiv preprint arXiv:2105.14244, 2021 | 4 | 2021 |
CASCONet: A Conference dataset D Luo, K Lyons arXiv preprint arXiv:1706.09485, 2017 | 4 | 2017 |
Group Sparse Optimal Transport for Sparse Process Flexibility Design. D Luo, T Yu, H Xu IJCAI, 6121-6129, 2023 | 2 | 2023 |