A Deep Generative Approach to Learning a Conditional Distribution

Presented by Jian Huang, Professor, Department of Statistics and Actuarial Science, Department of Biostatistics, University of Iowa

Wednesday, February 9, 2022
4:00 p.m. ET
Online

Conditional distribution is a fundamental quantity in statistics and machine learning that 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 using neural networks by matching appropriate joint distributions. 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, including the California housing, the MNIST, and the CelebA datasets, to illustrate its applications in conditional sample generation, uncertainty assessment of prediction, visualization of multivariate data, image generation and image reconstruction.

Speaker Bio

Dr. Jian Huang is Professor in the Department of Statistics and Actuarial Science and the Department of Biostatistics at the University of Iowa. His research interests include semiparametric models, statistical genetics, survival analysis, and analysis of high-dimensional data. Dr. Huang holds a PhD degree in Statistics from the University of Washington and is Fellow of the Institute of Mathematical Statistics and the American Statistical Association.