报告题目：Multi-task Learning in Vector-valued Reproducing Kernel Banach Spaces with the l1 Norm
报 告 人：林荣荣博士 广东工业大学
报告时间：2020年9月17日上午 10:45-11:20
报告地点：腾讯会议 ID：206 372 412
会议密码：0917
校内联系人：王蕊 rwang11@jlu.edu.cn
报告摘要：
Targeting at sparse multi-task learning, we consider regularization models with an l1 penalty on the coefficients of kernel functions. In order to provide a kernel method for this model, we construct a class of vector-valued reproducing kernel Banach spaces with the l1 norm. The notion of multi-task admissible kernels is proposed so that the constructed spaces could have desirable properties including the crucial linear representer theorem. Such kernels are related to bounded Lebesgue constants of a kernel interpolation question. We study the Lebesgue constant of multi-task kernels and provide examples of admissible kernels. Furthermore, we present numerical experiments for both synthetic data and real-world benchmark data to demonstrate the advantages of the proposed construction and regularization models. This is a joint work with Prof. Guohui Song (ODU) and Haizhang Zhang (SYSU).
报告人简介：
林荣荣于2017年6月在中山大学必威获得博士学位；2017年7月至2020年7月担任中山大学特聘副研究员；2020年8月开始加入广东工业大学应用必威，现为讲师。曾在加拿大阿尔伯塔大学交换学习一年和在美国奥多名尼奥大学短期学术访问两个月。研究方向为机器学习核函数方法和时频信号分析。目前主持国家自然科学青年基金项目等。
