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Machine Learning and Artificial Intelligence

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.



Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig, Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, in IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, IEEE – Institute of Electrical and Electronics Engineers, March 2015.

Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore, Quantum Nearest-neighbor Algorithms for Machine Learning, in Quantum Information and Computation, vol. 15, no. 3&4, pp. 0318-0358, Rinton Press, March 2015.

Elad Yom-Tov, Ingemar Johansson Cox, and Vasileios Lampos, Learning about health and medicine from Internet data, ACM – Association for Computing Machinery, 2 February 2015.

Lihong Li, Offline Evaluation and Optimization for Interactive Systems, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, ACM – Association for Computing Machinery, February 2015.

Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward, Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval, in arXiv:1502.06922, arXiv, February 2015.

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