120 citations · 169 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Bayesian Inference for Gamma Models
Jingyu He, Nicholas Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…
Data Augementation with Polya Inverse Gamma
Jingyu He, Nicholas G. Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in stati…
Deep Learning in Characteristics-Sorted Factor Models
Guanhao Feng, Jingyu He, Nicholas G. Polson +1
This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for co…
Weighted Bayesian Bootstrap for Scalable Bayes
Michael Newton, Nicholas G. Polson, Jianeng Xu
We develop a weighted Bayesian Bootstrap (WBB) for machine learning and statistics. WBB provides uncertainty quantification by sampling from a high dimensional posterior distributi…