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

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

Hamid Palangi, Li Deng, and Rabab K Ward, RECURRENT DEEP-STACKING NETWORKS FOR SEQUENCE CLASSIFICATION, IEEE Conference ChinaSIP, July 2014

Qihang Lin, Zhaosong Lu, and Lin Xiao, An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization, no. MSR-TR-2014-94, 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

More publications...