| Dag-gnn: Dag structure learning with graph neural networks Y Yu, J Chen, T Gao, M Yu ICML arXiv preprint arXiv:1904.10098, 2019 | 528 | 2019 |
| Efficient Markov Blanket Discovery and Its Application T Gao, Q Ji IEEE Transactions on Cybernetics, 2016 | 90 | 2016 |
| Generalized linear rule models D Wei, S Dash, T Gao, O Günlük ICML arXiv preprint arXiv:1906.01761, 2019 | 76 | 2019 |
| DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks D Wei, T Gao, Y Yu NeurIPS 20 arXiv preprint arXiv:2010.09133, 2020 | 75 | 2020 |
| Nonlocal kernel network (NKN): A stable and resolution-independent deep neural network H You, Y Yu, M D'Elia, T Gao, S Silling Journal of Computational Physics 469, 111536, 2022 | 68 | 2022 |
| DAGs with No Curl: An Efficient DAG Structure Learning Approach Y Yu, T Gao, N Yin, Q Ji ICML 21 arXiv preprint arXiv:2106.07197, 2021 | 64 | 2021 |
| Local causal discovery of direct causes and effects T Gao, Q Ji Advances in Neural Information Processing Systems 28, 2015 | 60 | 2015 |
| Proximal graphical event models D Bhattacharjya, D Subramanian, T Gao Advances in Neural Information Processing Systems 31, 2018 | 56 | 2018 |
| Local-to-global Bayesian network structure learning T Gao, K Fadnis, M Campbell International Conference on Machine Learning, 1193-1202, 2017 | 49 | 2017 |
| Type-augmented Relation Prediction in Knowledge Graphs Z Cui, P Kapanipathi, K Talamadupula, T Gao, Q Ji AAAI 21 arXiv preprint arXiv:2009.07938, 2020 | 48 | 2020 |
| Efficient score-based Markov blanket discovery T Gao, Q Ji International Journal of Approximate Reasoning 80, 277-293, 2017 | 45 | 2017 |
| Multi-step entity-centric information retrieval for multi-hop question answering R Das, A Godbole, D Kavarthapu, Z Gong, A Singhal, M Yu, X Guo, T Gao, ... Proceedings of the 2nd Workshop on Machine Reading for Question Answering …, 2019 | 42 | 2019 |
| Timeline summarization based on event graph compression via time-aware optimal transport M Li, T Ma, M Yu, L Wu, T Gao, H Ji, K McKeown Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 38 | 2021 |
| Characterization of Overlap in Observational Studies M Oberst, FD Johansson, D Wei, T Gao, G Brat, D Sontag, KR Varshney AISTATS, 2020 | 34 | 2020 |
| Do multi-hop readers dream of reasoning chains? H Wang, M Yu, X Guo, R Das, W Xiong, T Gao arXiv preprint arXiv:1910.14520, 2019 | 33 | 2019 |
| Identifying the discourse function of news article paragraphs WV Yarlott, C Cornelio, T Gao, M Finlayson Proceedings of the Workshop Events and Stories in the News 2018, 25-33, 2018 | 33 | 2018 |
| Event-Driven Continuous Time Bayesian Networks D Bhattacharjya, K Shanmugam, T Gao, N Mattei, KR Varshney, ... AAAI, 2020 | 26 | 2020 |
| Parallel Bayesian network structure learning T Gao, D Wei International Conference on Machine Learning, 1685-1694, 2018 | 26 | 2018 |
| Knowledge-augmented deep learning and its applications: A survey Z Cui, T Gao, K Talamadupula, Q Ji IEEE Transactions on Neural Networks and Learning Systems, 2023 | 20 | 2023 |
| Multi-step entity-centric information retrieval for multi-hop question answering A Godbole, D Kavarthapu, R Das, Z Gong, A Singhal, H Zamani, M Yu, ... arXiv preprint arXiv:1909.07598, 2019 | 20 | 2019 |
Debarun BhattacharjyaResearcher, IBM T.J. Watson Research CenterVerified email at us.ibm.com