E-mail: liuli [@] eecs [.] oregonstate [.] edu
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My research is mainly focused on machine learning problems in computational sustainability. I have worked on the following problems: superset label learning, probabilistic modeling at the population level, and integration of prediction and decision-making. I have put many machine learning tools in my toolbox, such as inference and learning of graphical models, learning theory, and optimization (convex analysis and mixed integer programming).
Liu, L.-P., Dietterich, T.G., Li, N., and Zhou, Z.-H.. Transductive Optimization of Top k Precision.arXiv:1510.05976 [cs.LG]. 2015.
Liu, L.-P., Quanz, B., Xing, D., Deshpande, A., and Liu, X.. Predicting Weeks-Of-Supply via Sequence Aggregating. In: Proceedings of the 2015 INFORMS Workshop on Data Mining and Analytics. 2015.
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 (ECML PKDD'2015).
Liu, L.-P., Sheldon, D., and Dietterich, T.. Gaussian Approximation of Collective Graphical Models. In: The 31st International Conference on Machine Learning (ICML'2014). [code]
Liu, L.-P. and Dietterich, T.. Learnability of the Superset Label Learning Problem. In: The 31st International Conference on Machine Learning (ICML'2014).
Liu, L.-P. and Dietterich, T.. A Conditional Multinomial Mixture Model for Superset Label Learning. In: 2012 Conference on Neural Information Processing Systems (NIPS'2012). [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 (SDM'12).
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 (AAAI'2011)
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 (ICDM'09).
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 (ICDM'08)
Jiang, Y. and Liu, L.-P.. Survey of Data Mining on Data Stream. Journal of Jiangnan University(Natural Science Edition).
|Variable definition||Use after definition||Use after definition, or use with immediate explanation|
|Variable name||Multiple characters, straightforward meaning for easy debugging||Single character with different fonts and cases, uniform styles for easy reading|
|Variable semantics||Expressing program logic, can be simple types or objects||As simple as possible, e.g. real number, vector, set, ...|
|Functions/Equations||Flow of clauses, such as if, for, and functions||Composition of simple operations, such as min, max, sum, and product|
|Emphasis||Efficiency and clearness||Clearness|
I received my B.S. in computer science from Hebei University of Technology in 2006. After three years' study in LAMDA group, I received my M.S. from Nanjing Univeristy in 2009. My advisor was Prof. Jiang and Zhi-Hua Zhou, Yuan. Then I worked in Alibaba for one year and a half. After that, I began to pursue my Ph.D. in Oregon State University. My advisor is Prof. Thomas Dietterich.
Last updated 01/29/2016, Liping Liu