CMX Student/Postdoc Seminar

Gaussian process regression (GPR) is a well-studied and commonly used kernel regression method. Among its advantages are profound theoretical basis, ability to perform well with relatively small amounts of data and built-in uncertainty quantification. However, it suffers from high computational complexity, limited expressiveness and some other shortcomings, such as poor performance in high dimensions. In this talk, I will give a brief introduction to Kernel Analog Forecasting (KAF), another kernel regression method that was developed independently in the last six years, and discuss connections between GPR and KAF, and how it is possible to overcome the first two drawbacks of GPR. I will also give a brief comparison of the two different uncertainty quantification approaches that these methods use.