学术报告：Large-Scale Low-Rank Gaussian Process Prediction with Support Points

报告时间：11月29日（星期五）上午10:00-11:30

报告地点：沙河校区，二教107

报告人：代文林，中国人民大学统计与大数据研究院，副教授

报告摘要：Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explaining the influence of these two factors on predictive processes are lacking. In this paper, the asymptotic prediction performance of the predictive process and Gaussian process predictions is derived and the impacts of the selected knots and estimated covariance are studied. We suggest the use of support points as knots, which best represent data locations. Extensive simulation studies demonstrate the superiority of support points and verify our theoretical results. Real data of precipitation and ozone are used as examples, and the efficiency of our method over other widely used low-rank approximation methods is verified.

报告人简介：代文林，中国人民大学统计与大数据研究院预聘副教授，国家治理大数据和人工智能创新平台副主任。主要研究方向为非参数统计、复杂数据分析与应用统计。以主要作者身份在Journal of the American Statistical Association, Journal of Machine Learning Research, Statistical Science, Science China Mathematics等一流统计学与机器学习期刊上发表论文30余篇。主持国家自然科学基金和国家社科基金项目。曾获得泛华统计协会国际大会Young Researcher Award。担任中国现场统计研究会统计调查分会理事，统计交叉科学分会理事。

撰稿人：刘洁

审稿人：邓露