题 目：Extensions of the Mondrian Process
时 间：2022年11月3日（周四） 10:00-11:00
地 点：线上，腾讯会议：580-704-066
报告人：葛淑菲 助理教授 上海科技大学
主持人：王小舟 助理教授
主 办：统计学院
摘 要：
Decision trees and the Mondrian processes (MPs) are powerful space-partitioning methods for relational data in multi-dimensional space. These methods are based on recursively cutting a domain, the flexibility of these methods is often limited by the requirement that the cuts be axis aligned. The binary space partitioning (BSP)-tree process was recently introduced as a generalization of the MP for space partitioning with non-axis aligned cuts in the two-dimensional space. Motivated by these processes, we propose the Random Tessellation Process (RTP), a framework that includes the MP and the BSP-tree process as special cases. We derive a sequential Monte Carlo algorithm for inference and provide random forest versions. The RTP is self-consistent and can relax axis-aligned constraints, allowing complex inter-dimensional dependence to be captured in multi-dimensional space. Moreover, we propose a novel parallel Bayesian nonparametric approach to split a two-dimensional domain with the Bézier curves in the framework of space partitioning methods, enabling complex data-shapes to be acquired.
报告人简介：
Shufei Ge is an assistant professor at the Institute of Mathematical Sciences, ShanghaiTech University, where she has been a faculty member since Sep. of 2020. Before that, she received her Ph.D. in statistics at Simon Fraser University, Canada. Her research interest involves Bayesian statistics, statistical machine learning methods and computational biology. She has published papers in statistical journals and machine learning conferences, including the Advances in Neural Information Processing Systems, Biometrics, Bioinformatics, and Journal of Computational and Graphical Statistics.