Machine Learning

Automated reasoning and the applications of decision making


We pursue research on automated reasoning, adaptation, and the theories and applications of decision making and learning. Our research goals include learning from data and data mining. By building software that automatically learns from data, we design applications that have new functions and flexibility. Our research focuses on using statistical methods for the development of more advanced, intelligent computer systems.

Publications

Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen, Modeling Interestingness with Deep Neural Networks, EMNLP, October 2014

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen, Knowledge Graph and Text Jointly Embedding, in The 2014 Conference on Empirical Methods on Natural Language Processing, ACL – Association for Computational Linguistics, October 2014

Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek, Typed Tensor Decomposition of Knowledge Bases for Relation Extraction, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ACL – Association for Computational Linguistics, October 2014

Michael Auli, Michel Galley, and Jianfeng Gao, Large-scale Expected BLEU Training of Phrase-based Reordering Models, EMNLP, October 2014

Lihong Li, Rémi Munos, and Csaba Szepesvari, On Minimax Optimal Offline Policy Evaluation, no. MSR-TR-2014-124, 15 September 2014

More publications...