1k citations · 1.3k across the 26 of their papers we have counts for
7 papers · 1 filter
Generative Causal Inference
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent pos…
Deep Partial Least Squares for Instrumental Variable Regression
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural net…
Bayesian Learning: A Selective Overview
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali +4
This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rap…
Merging Two Cultures: Deep and Statistical Learning
Anindya Bhadra, Jyotishka Datta, Nick Polson +2
Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for…
Deep Learning Partial Least Squares
Nicholas Polson, Vadim Sokolov, Jianeng Xu
High dimensional data reduction techniques are provided by using partial least squares within deep learning. Our framework provides a nonlinear extension of PLS together with a dis…
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…
Bayesian Regularization: From Tikhonov to Horseshoe
Nicholas G. Polson, Vadim Sokolov
Bayesian regularization is a central tool in modern-day statistical and machine learning methods. Many applications involve high-dimensional sparse signal recovery problems. The go…