关于举办哥伦比亚大学John Paisley教授学术报告的通知

报告题目：Randomized Single-Layer Gaussian Process Neural Networks for Data Science Applications

报 告 人：John Paisley（Columbia University哥伦比亚大学）

报告时间：2025年6月3日（星期二）下午2:30-4:00

报告地址：五山校区逸夫科学馆504报告厅

报告邀请人：曾德炉教授

邀约单位：电子与信息学院

报告摘要：

This talk discuss applications of the random Fourier feature construction of the Gaussian process to some fundamental machine learning problems. This includes (1) interpretable classification and regression with neural additive models, (2) collaborative filtering with nonlinear matrix factorization, and (3) density estimation using GP-tilted functions. For each of these problems, a single-layer neural network based on the Gaussian process is employed for which the randomized weights remove the need to learn network parameters. As a result, the learning algorithms are drastically simplified while retaining much of the learning power of nonlinear models for the low-dimensional problems considered.

报告人简介：

Prof. John Paisley is an Associate Professor in the Department of Electrical Engineering at Columbia University, where he is also a member of the Data Science Institute. His research interests include Bayesian models and inference, with applications to machine learning problems. Before joining Columbia in 2013, he was a postdoctoral researcher in the computer science departments at Princeton University and UC Berkeley. He received the BSE and PhD degrees in Electrical and Computer Engineering from Duke University in 2004 and 2010, respectively.