报告名称：Robust Gradient-based Markov Subsampling

主办单位：英国立博官网中文版

报告专家：龚铁梁

专家所在单位：渥太华大学

报告时间：2019年11月15日(周五)上午9:00-10:30

报告地点：英国立博官网中文版201报告厅

专家简介：

龚铁梁博士，渥太华大学博士后研究员，2018年在西安交通大学获博士学位，2016/10-2017/10曾在美国密歇根大学安娜堡分校访问。研究方向主要包括统计学习理论以及机器学习。其研究成果主要发表在IEEE Trans.on Cybernetics, Neural Computation, AAAI等国际知名期刊和顶级会议上。目前的研究兴趣聚焦于subsampling方法在大数据背景下的理论及应用。

报告摘要：

Subsampling is a widely used and effective method to deal with the challenges brought by big data. Most subsampling procedures are designed based on the importance sampling framework, where samples with high importance measures are given corresponding sampling probabilities. However, in the highly noisy case, these samples may cause an unstable estimator whichcould lead to a misleading result. In this talk, We propose a gradient Markov subsampling (GMS) algorithm to achieve robust estimation. The core idea is to construbct a subset which allows us to conservatively correct a crude initial estimate towards the true signal. Specifically, GMS selects samples with small gradients via a probabilistic procedure, constructing a subset that is likely to exclude noisy samples and provide a safe improvement over the initial estimate. We show that the GMS estimator is statistically consistent at a rate which matches the optimal in the minimax sense. The promising performance of GMS is supported by simulation studies and real data examples.

邀请人：邹斌