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.



, , jibian, bingao, , and tyliu, RC-NET: A General Framework for Incorporating Knowledge into Word Representations, Choose..., November 2014

Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , CIKM, November 2014

Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi, An Eye-tracking Study of User Interactions with Query Auto Completion, in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), ACM – Association for Computing Machinery, November 2014

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

More publications...