第一届北大–清华统计论坛
2016 PKU-Tsinghua Colloquium On Statistics

为促进北大和清华两校教师之间以及学生之间的交流和合作， 促进统计学者的共同进步，促进统计学科的发展，迎接新时代统计学面临的机遇和挑战，第一届北大–清华统计论坛将于2016年12月16日在北京大学国际数学中心甲乙丙楼多功能会议室召开。北京大学统计科学中心科学委员会主席、美国国家科学院院士、美国加州伯克利大学统计系郁彬教授和清华大学统计中心主任、美国哈佛大学统计系刘军教授将做大会报告。

主办方

• 北京大学统计科学中心
• 清华大学统计学研究中心
• 北京大学数学科学学院
• 北京国际数学中心

会议日程

• 14:00-15:00  郁彬教授特邀报告
• 15:00-15:30  茶歇
• 15:30-16:30  刘军教授特邀报告
  

特邀报告

（一）

报告题目：Artificial Neurons Meet Real Neurons: Pattern Selectivity of V4
摘  要：Vision in humans and in non-human primates is mediated by a constellation of hierarchically organized visual areas. One important area is V4, a large retinotopically-organized area located intermediate between primary visual cortex and high-level areas in the inferior temporal lobe. V4 neurons have highly nonlinear response properties. Consequently, it has been difficult to construct quantitative models that accurately describe how visual information is represented in V4. To better understand the
filtering properties of V4 neurons we recorded from 71 well isolated cells stimulated with natural images. We fit predictive models of neuron spike rates using transformations of natural images learned by a convolutional neural network (CNN). The CNN was trained for image classification on the ImageNet dataset. To derive a model for each neuron, we first propagate each of the stimulus images forward to an inner layer of the CNN. We use the activations of the inner layer as the feature (predictor) vector in a high dimensional regression, where the response rate of the V4 neuron is taken as the response vector. Thus, the final model for each neuron consists of a multilayer nonlinear transformation provided by the CNN, and one final linear layer of weights provided by regression. We find that models using the first two layers of three well-known CNNs provide better predictions of responses of V4 neurons than those obtained using a conventional Gabor-like wavelet model. To characterize the spatial and pattern selectivity of each V4 neuron, we both explicitly optimize the input image to maximize the predicted spike rate, and visualize the selected filters of the CNN. We also perform dimensionality reduction by sparse PCA to visualize the population of neurons. Finally, we show the stability of our analysis across the three CNNs and regression methods Lasso and Ridge, and conclude that the V4 neurons are tuned to a remarkable diversity of shapes such as curves, blobs, checkerboard patterns, and V1-like gratings.
报告人：郁彬，加州大学伯克利分校统计系及电气工程与计算机科学系校长教授，加州大学伯克利分校统计系前系主任。 北京大学统计科学中心科学委员会主席。 她同时是北京大学微软统计与信息技术教育部–微软重点实验室的创办者及联席主任。她与基因组学、神经科学、医学领域科学家合作进行跨学科研究，开发了统计和机器学习方法/算法和理论，并与领域知识以及量化批判思维结合以解决这些领域中的数据问题。郁彬教授是美国国家科学院和美国艺术与科学学院两院院士。2006 年当选Guggenheim Fellow，2011 年受邀在 ICIAM （The International Council for Industrial and Applied Mathematics，国际工业与应用数学大会）作特邀演讲， 2012 年作了伯努利协会的图基纪念演讲（Turkey Memorial Lecture of the Bernoulli Society）， 2016 年作IMS（Institution of Mathematical Statistics，数理统计协会） Rietz 演讲。郁彬教授曾于 2013-2014 年出任 IMS 主席，也是 IMS、ASA（ American Statistical Association，美国统计协会）、AAAS（American Association for the Advancement of Science，美国科学促进会）和 IEEE（Institute of Electrical and Electronics Engineers，电气和电子工程师协会）的会士。

（二）

报告题目：Generalized R-squared for detecting dependence
摘  要：Detecting dependence between two random variables is a fundamental problem.Although the Pearson correlation is effective for capturing linear dependency, it can be entirely powerless for detecting nonlinear and/or heteroscedastic patterns. We introduce anew measure, G-squared, to test whether two univariate random variables are independent and to measure the strength of their relationship. The G-squared is almost identical to the square of the Pearson correlation coefficient, R-squared, for linear relationships with constant error variance, and has the intuitive meaning of the piecewise R-squared between the variables. It is particularly effective in handling nonlinearity and heteroscedastic errors. We propose two estimators of G-squared and show their consistency. Simulations demonstrate that G-squared estimators are among the most powerful test statistics compared with several state-of-the-art methods.

报告人：刘军，哈佛大学统计系和生物统计系教授，清华大学统计中心主任。 是世界生物统计和生物信息学领域的著名专家。他们实验室是转录因子-DNA 序列结合位点的预测课题的先驱。用计算机方法和统计学方法预测的这些位点经过实验室验证属实，目前已经预测的有：大肠杆菌（ E. Coli）、Basillus Subtilis、酵母和人类转录因子结合位点。此外，他们预测的部分蛋白结构也已经获得实验室证实。 在贝叶斯方法、蒙特卡罗方法、生物信息学、遗传学等领域做出了一系列奠基性工作。对统计理论、复杂系统优化、基因组学、信号处理等领域产生了非常深
远的影响。刘军教授 2012 年获得泛华统计协会杰出成就奖， 2010 年获得华人数学界的最高荣誉晨星应用数学金奖， 2005 年被美国统计协会(ASA)选为“ASA FELLOW”；于 2002年获得北美五个统计学会联合设立的 “COPSS Presidents’ Award”。 2001 年刘军教授完成了自己的英文著作《科学计算中的蒙特卡罗策略》。此书现已成为哈佛大学、斯坦福大学及其他高等学府的教科书。2010 至 2013 年间担任《 JASA》的联合主编，是国际数理统计学会会士（ IMS fellow）和美国统计学会会士（ ASA fellow）。