| A conditional multinomial mixture model for superset label learning L Liu, T Dietterich Advances in neural information processing systems 25, 2012 | 173 | 2012 |
| Learnability of the superset label learning problem L Liu, T Dietterich International Conference on Machine Learning, 1629-1637, 2014 | 74 | 2014 |
| Incorporating boosted regression trees into ecological latent variable models R Hutchinson, LP Liu, T Dietterich Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 1343-1348, 2011 | 66 | 2011 |
| Least square incremental linear discriminant analysis LP Liu, Y Jiang, ZH Zhou 2009 Ninth IEEE International Conference on Data Mining, 298-306, 2009 | 53 | 2009 |
| Gan ensemble for anomaly detection X Han, X Chen, LP Liu Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4090-4097, 2021 | 34 | 2021 |
| Tefe: A time-efficient approach to feature extraction LP Liu, Y Yu, Y Jiang, ZH Zhou 2008 Eighth IEEE International Conference on Data Mining, 423-432, 2008 | 24 | 2008 |
| Kriging convolutional networks G Appleby, L Liu, LP Liu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3187-3194, 2020 | 19 | 2020 |
| Context selection for embedding models L Liu, F Ruiz, S Athey, D Blei Advances in Neural Information Processing Systems 30, 2017 | 17 | 2017 |
| Transductive optimization of top k precision LP Liu, TG Dietterich, N Li, ZH Zhou arXiv preprint arXiv:1510.05976, 2015 | 17 | 2015 |
| Gaussian approximation of collective graphical models L Liu, D Sheldon, T Dietterich International Conference on Machine Learning, 1602-1610, 2014 | 15 | 2014 |
| Predicting physics in mesh-reduced space with temporal attention X Han, H Gao, T Pffaf, JX Wang, LP Liu arXiv preprint arXiv:2201.09113, 2022 | 11 | 2022 |
| Zero-inflated exponential family embeddings LP Liu, DM Blei International Conference on Machine Learning, 2140-2148, 2017 | 10 | 2017 |
| Stochastic iterative graph matching L Liu, MC Hughes, S Hassoun, L Liu International Conference on Machine Learning, 6815-6825, 2021 | 9 | 2021 |
| Pathway-activity likelihood analysis and metabolite annotation for untargeted metabolomics using probabilistic modeling R Hosseini, N Hassanpour, LP Liu, S Hassoun Metabolites 10 (5), 183, 2020 | 8 | 2020 |
| Amortized variational inference with graph convolutional networks for gaussian processes L Liu, L Liu The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 8 | 2019 |
| Constructing training sets for outlier detection LP Liu, XZ Fern Proceedings of the 2012 SIAM International Conference on Data Mining, 919-929, 2012 | 8 | 2012 |
| Learning graph representations of biochemical networks and its application to enzymatic link prediction J Jiang, LP Liu, S Hassoun Bioinformatics 37 (6), 793-799, 2021 | 7 | 2021 |
| Bayesian active clustering with pairwise constraints Y Pei, LP Liu, XZ Fern Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 6 | 2015 |
| Order matters: Probabilistic modeling of node sequence for graph generation X Chen, X Han, J Hu, FJR Ruiz, L Liu arXiv preprint arXiv:2106.06189, 2021 | 5 | 2021 |
| Using graph neural networks for mass spectrometry prediction H Zhu, L Liu, S Hassoun arXiv preprint arXiv:2010.04661, 2020 | 5 | 2020 |
Thomas DietterichDistinguished Professor (Emeritus), Computer Science, Oregon State UniversityVerified email at cs.orst.edu
