Scalable Gaussian Process Analysis and Nonparametric Learning
Time: 2016-11-03 15:49:00
Title： Scalable Gaussian Process Analysis and Nonparametric Learning
Speaker： Jie Chen, IBM Thomas J. Watson Research Center
Abstract： Gaussian processes are the cornerstone of statistical analysis, broadly used in disciplines such as scientific computing and machine learning. Example applications include quantifying the simulation uncertainty produced by stochastic inputs, designing effective computer experiments with a large number of parameters, and recognizing patterns in speech, image, and text data. The oretically grounded, Gaussian processes incur a significant challenge in computations, because the arithmetic costs are generally cubic in the data size. In this talk, I present several lines of work that addresses the challenge facing large-scale data, for tasks such as sampling, prediction, and parameter estimation. These efforts hint on a quest for unifying treatments of kernel matrices that are fully dense but structured. I will conclude the talk by presenting an approach that establishes a linear-complexity framework for handling these matrices, particularly in the high dimensional setting that is currently a major hurdle.
Time： 2016-11-03 10:30--11:30
Location：School of Mathematical Sciences