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.



Li Deng and John C. Platt, Ensemble Deep Learning for Speech Recognition, Proc. Interspeech, September 2014

Yanjie Fu, Hui Xiong, Yong Ge, Zijun Yao, Yu Zheng, and Zhi-Hua Zhou, Exploiting Geographic Dependencies for Real Estate Appraisal: A Mutual Perspective of Ranking and Clustering, ACM – Association for Computing Machinery, August 2014

Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan, and Ryen White, Modeling Action-level Satisfaction for Search Task Satisfaction Prediction, in ACM SIGIR 2014, ACM, July 2014

Tomáš Kocák, Michal Valko, Rémi Munos, and Shipra Agrawal, Spectral Thompson Sampling, in 28th AAAI Conference on Artificial Intelligence (AAAI 2014), AAAI - Association for the Advancement of Artificial Intelligence, July 2014

Long Tran-Thanh, Lampros Stavrogiannis, Victor Naroditskiy, Valentin Robu, Nicholas R Jennings, and Peter Key, Efficient Regret Bounds for Online Bid Optimisation in Budget-Limited Sponsored Search Auctions, in uai2014, 30th Conf. on Uncertainty in AI, AUAI, July 2014

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