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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.

Videos

Publications

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.

Lihong Li, Jin Young Kim, and Imed Zitouni, Toward Predicting the Outcome of an A/B Experiment for Search Relevance, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, ACM – Association for Computing Machinery, February 2015.

Asli Celikyilmaz and Dilek Hakkani-Tur, INVESTIGATION OF ENSEMBLE MODELS FOR SEQUENCE LEARNING, IEEE – Institute of Electrical and Electronics Engineers, 1 February 2015.

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