题目一：Coefficient-based lq- regularized direct learning for estimating individual treatment rule
内容简介：The aim of precision medicine is to identify the best treatment approach for each individual patient by taking into account their unique characteristics. This involves developing a decision function, known as an individual treatment rule (ITR), which maximizes the expected clinical outcome. Direct learning (D-learning) is one of the main algorithms estimating the optimal ITR. In this talk, we mainly study the coefficient-based D-learning with lq -regularizer (where 1 < q ≤ 2) with unbounded clinical outcome. We establish the error bounds for the algorithm by constructing the stepping stone function and applying concentration inequality with empirical covering numbers. Fast learning rates are derived explicitly under a moment condition on the clinical outcome.
报告人：向道红
报告人简介：浙江师范大学数学科学学院教授，博士生导师，德国洪堡学者，浙江省高校中青年学科带头人，浙江省应用数学研究会副理事长。于2009年2月获香港城市大学博士学位，2009-2010年在香港中文大学作博士后，2010年3月入职浙江师范大学至今。研究领域为统计学习理论、稳健统计等。在《Journal of Machine Learning Research》《Journal of Approximation Theory》《Advances in Computational Mathematics》《Journal of Multivariate Analysis》《Science China Mathematics》等国内外学术刊物上发表论文多篇。主持完成国家自然科学基金面上项目2项、青年基金1项、浙江省自然科学基金1项。
题目二：Graph Fourier Transform On Directed Graphs
内容简介：Graph signal processing provides an innovative framework to process data on graphs. The widely used graph Fourier transform on the undirected graph is based on the eigen-decomposition of the Laplacian. In many engineering applications, the data is time-varying and pairwise interactions among agents of a network are not always mutual and equitable, such as the interaction data on a social network. Then the graph Fourier transform on directed graph is in demand and it should be designed to reflect the spectral characteristic for different directions, decompose graph signals into different frequency components, and to efficiently represent the graph signal by different modes of variation. In this talk, I will present our recent work on the graph Fourier transforms on directed graphs which are based on the singular value decompositions of the Laplacians.
报告人：成诚
报告人简介：中山大学数学学院副教授，在此之前在美国杜克大学和美国统计和应用数学研究所做博士后研究，期间合作导师是美国三院院士Ingrid Daubechies 教授。成诚毕业于中佛罗里达大学数学系，指导老师是孙颀彧教授和李欣教授，她的主要研究方向为应用调和分析，特别是采样理论，相位恢复以及图信号处理中的数学理论，目前已有多篇论文发表在 Applied and Computational Harmonic Analysis, Journal of Functional Analysis, Journal of Fourier Analysis and Applications, IEEE Transaction on Signal Processing, Signal Processing, and IEEE Signal Processing letters 等。现主持国家自然科学基金一项，广东省自然科学基金一项。
时 间：2023年10月16日（周一）上午9：30 始
地 点：腾讯会议：388-854-158
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