题目: Stochastic Tree Ensembles For Prediction and Heterogeneous Treatment Effect Estimation

报告人: 何靖宇 (香港城市大学 助理教授)

时间: 2023年5月12日(星期五) 下午16:00-17:00

报告地点：bat365官网登录一楼135报告厅

报告摘要: This article develops a novel stochastic tree ensemble method for nonlinear regression, referred to as accelerated Bayesian additive regression trees, or XBART. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partitioning algorithms, XBART attains state-of-the-art performance at prediction and function estimation. Simulation studies demonstrate that XBART provides accurate point-wise estimates of the mean function and does so faster than popular alternatives, such as BART, XGBoost, and neural networks (using Keras) on a variety of test functions. Additionally, it is demonstrated that using XBART to initialize the standard BART MCMC algorithm considerably improves credible interval coverage and reduces total run-time. Finally, two basic theoretical results are established: the single tree version of the model is asymptotically consistent and the Markov chain produced by the ensemble version of the algorithm has a unique stationary distribution.

个人简介: 何靖宇，博士，现为香港城市大学商学院管理科学系助理教授，同时在香港城市大学数据科学学院、康奈尔大学金融科技倡议、香港人工智能金融科技实验室有限公司等机构担任访问或联合职位。获中国科学技术大学获统计学学士学位，芝加哥大学统计学硕士学位，芝加哥大学布斯商学院工商管理硕士学位、计量经济学博士学位。从事贝叶斯统计、机器学习特别是树集成模型、金融机器学习等方向的研究工作，成果发表于JASA，JOE，JBES等统计、计量国际知名期刊。