Publication List

Chen, X.; Chen, X; Liu, L.-P.; Interpretable Node Representation with Attribute Decoding. Transactions on Machine Learning Research. 2022 (TMLR’2022). [paper]

Liu, L.; Han, X.; Zhou, D.; and Liu, L.-P.. Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. Transactions on Machine Learning Research. 2022 (TMLR’2022). [paper][code]

Li, X.; Liu, L.-P.; and Hassoun, S.. Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme–Substrate Interaction Prediction. Bioinformatics 38 (10). 2022.

Han, X.; Gao, H.; Pffaf, T.; Wang, J.-X.; and Liu, L.-P.. Predicting Physics in Mesh-reduced Space with Temporal Attention. In: Proceedings of the 10th International Conference on Learning Representations. 2022 (ICLR’2022). [paper]

Liu, L.-P.; Gu, R.; and Hu, X.. Ladder Polynomial Neural Networks. arXiv:2106.13834. 2021.

Chen, X.; Han, X.; Hu, J.; Ruiz, F.; and Liu, L.-P.. Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. In: Proceedings of the 38th International Conference on Machine Learning. 2021 (ICML’2021). [paper][code]

Liu, L.; Hughes, M.C.; Hassoun, S.; and Liu, L.-P.. Stochastic Iterative Graph Matching. In: Proceedings of the 38th International Conference on Machine Learning. 2021 (ICML’2021). [paper][code]

Han, X.; Chen X.; and Liu L.-P.. GAN Ensemble for Anomaly Detection. In: Proceedings of the AAAI Conference on Artificial Intelligence 35. 2021 (AAAI’2021). [paper][code]

Zhu, H.; Liu, L.-P.; and Hassoun, S. Using Graph Neural Networks for Mass Spectrometry Prediction. Machine Learning in Computational Biology, 2020. [paper]

Liu, L. and Liu, L.-P.. Localizing and Amortizing: Efficient Inference for Gaussian Processes. Asian Conference on Machine Learning, 2020 (ACML’2020). [paper]

Jiang, J.; Liu, L.-P.; and Hassoun S.. Learning Graph Representations of Biochemical Networks and Its Application to Enzymatic Link Prediction. Bioinformatics, btaa881, Oct., 2020.[paper]

Hosseini, R.; Hassanpour, N.; Liu, L.-P.; and Hassoun, S. Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics Using Probabilistic Modeling. Metabolites 2020, 10, 183. [paper]

Appleby, G.; Liu, L.; Liu, L.-P.. Kriging Convolutional Networks. In: the 34th AAAI Conference on Artificial Intelligence. 2020 (AAAI’2020). [paper][code]

Jiang, J.; Liu, L.-P.; and Hassoun, S.. Predicting Reactions for Biochemical Networks Using Graph Embeddings. In: the 14th Machine Learning in Computational Biology Meeting (MLCB’2019).

Liu, L. and Liu, L.-P.. Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. In: the 22nd International Conference on Artificial Intelligence and Statistics. 2019 (AISTATS’2019). [paper][code]

Liu, L.-P.; Ruiz, J.R. F.; Athey, S. and Blei, D.. Context Selection for Embedding Models. In: 2017 Conference on Neural Information Processing Systems. 2017 (NIPS’2017). [paper] [code]

Liu, L.-P. and Blei, D.. Zero-Inflated Exponential Family Embeddings. In: Proceedings of the 34th International Conference on Machine Learning. 2017 (ICML’2017). [paper][code]

Liu, L.-P.. Machine Learning Methods for Computational Sustainability. PhD Thesis, Oregon State University, 2016. [link]

Liu, L.-P.; Dietterich, T.G.; Li, N.; and Zhou, Z.-H.. Transductive Optimization of Top k Precision. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016 (IJCAI’2016). [paper][code]

Pei, Y, Liu, L.-P.; and Fern, X.. Bayesian Active Clustering with Pairwise Constraints. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2015 (ECML PKDD’2015).

Liu, L.-P.; Sheldon, D.; and Dietterich, T.. Gaussian Approximation of Collective Graphical Models. In: Proceedings of the 31st International Conference on Machine Learning. 2014 (ICML’2014). [paper][code]

Liu, L.-P. and Dietterich, T.. Learnability of the Superset Label Learning Problem. In: Proceedings of the 31st International Conference on Machine Learning. 2014 (ICML’2014). [paper]

Liu, L.-P. and Dietterich, T.. A Conditional Multinomial Mixture Model for Superset Label Learning. In: 2012 Conference on Neural Information Processing Systems. 2012 (NIPS’2012). [paper][supp. marterial][code]

Liu, L.-P. and Fern, X.. Constructing Training Set for Outlier Detection. In: Proceedings of the 12th SIAM International Conference on Data Mining 2012 (SDM’2012). [paper]

Hutchinson, R.; Liu, L.-P.; and Dietterich, T.. Incorporating Boosted Regression Trees into Ecological Latent Variable Models. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence. 2011 (AAAI’2011). [paper]

Liu, L.-P.; Jiang, Y.; and Zhou, Z.-H.. Least Square Incremental Linear Discriminant Analysis. In: Proceedings of the 9th IEEE International Conference on Data Mining. 2009 (ICDM’2009). [paper]

Liu, L.-P.; Yu, Y.; Jiang, Y.; and Zhou, Z.-H.. TEFE: A Time-Efficient Approach to Feature Extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining. 2008 (ICDM’2008). [paper]