主题：A Deep Generative Approach to Conditional Learning
主讲人：黄坚 讲席教授（香港理工大学）
主持人：杨广仁教授（暨南大学）
会议时间：2022年11月29日（周二）15：00—17：00
会议工具：腾讯会议（ID：352-640-310）
摘要
Conditional distribution is a fundamental quantity in statistics and machine learning, which provides a full description of the relationship between a response and a predictor. There is a vast literature on conditional density estimation. A common feature of the existing methods is that they seek to estimate the functional form of the conditional density. We propose a deep generative approach to learning a conditional distribution by estimating a conditional generator, so that a random sample from the target conditional distribution can be obtained by transforming a sample from a reference distribution. The conditional generator is estimated nonparametrically with neural networks by matching appropriate joint distributions using a discrepancy measure. There are several advantages of the proposed generative approach over the classical methods for conditional density estimation, including: (a) there is no restriction on the dimensionality of the response or predictor, (b) it can handle both continuous and discrete type predictors and responses, and (c) it is easy to obtain estimates of the summary measures of the underlying conditional distribution by Monte Carlo. We conduct numerical experiments to validate the proposed method and using several benchmark datasets to illustrate its applications in conditional sample generation, uncertainty quantification of prediction, visualization of multivariate data, image generation and image reconstruction.
★主讲人简介★
黄坚，香港理工大学应用数学系讲席教授，获得西雅图华盛顿大学统计学博士学位。他的研究兴趣包括机器学习、高维统计、计算统计、生物统计学和生物信息学。他在统计学、生物统计学、机器学习、生物信息学和计量经济学领域发表多篇文章。2015年至2019年，他入选Web of Science group at Clarivate数学领域的高被引学者榜，并被Elsevier BV和斯坦福大学列入世界上引用最多科学家的前2%（2021）。他是美国统计协会会士和国际数理统计学会会士。
