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邀请，北京三星通信技术研究院赵季研究员于2015年5月11日—5月14日访问我校，并作以下学术报告。
题 目：基于核方法的双样本检验、快速算法及几何解释
时 间：5月14日（周四）上午 10:30
地 点：理科楼122
摘 要：
The maximum mean discrepancy (MMD) is a recently proposed test statistic for two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform. Taking advantage of sampling of Fourier transform, FastMMD decreases the time complexity for MMD calculation from quadratic to linear.We have further provided a geometric explanation for our method, namely ensemble of circular discrepancy, which facilitates us to understand the insight of MMD, and is hopeful to help arouse more extensive metrics for assessing two-sample test. Experimental results substantiate that FastMMD is with similar accuracy as exact MMD, while with faster computation speed and lower variance than the existing MMD approximation methods.
报告人简介：
赵季，2012年于华中科技大学自动化学院获得博士学位。2012年至2014年在美国卡内基-梅隆大学（CMU）机器人研究所从事博士后研究，目前在北京三星通信技术研究院担任研究员。研究兴趣包括：计算机视觉、机器学习，特别是图像匹配、kernel methods等。赵季博士在计算机视觉的高水平期刊和会议上发表过多篇论文，包括IEEE CVPR、Neural Computation、IEEE TIP、IEEE TSP等，担任多个知名期刊和会议的审稿人。个人主页：//sites.google.com/site/drjizhao/.
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