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Found 17 papers, 7 papers with code
Deep Partial Least Squares for Empirical Asset Pricing
This non-linear factor structure is extracted by using projected least squares to jointly project firm characteristics and asset returns on to a subspace of latent factors and using deep learning to learn the non-linear map from the factor loadings to the asset returns.
Data Augementation with Polya Inverse Gamma
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
Horseshoe Regularization for Machine Learning in Complex and Deep Models
We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Data Augmentation for Bayesian Deep Learning
Our methodology is compared to traditional stochastic gradient descent with back-propagation.
Deep Fundamental Factor Models
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling.
Bayesian Regularization: From Tikhonov to Horseshoe
Bayesian regularization is a central tool in modern-day statistical and machine learning methods.
Methodology
Deep Learning
Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models.
Deep Learning in Characteristics-Sorted Factor Models
This paper presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing.
Methodology
Deep Learning for Predicting Asset Returns
Deep learning searches for nonlinear factors for predicting asset returns.
Lasso Meets Horseshoe
The goal of our paper is to survey and contrast the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization methodologies.
Methodology Primary 62J07, 62J05, Secondary 62H15, 62F03
Bayesian $l_0$-regularized Least Squares
To illustrate our methodology, we provide simulation evidence and a real data example on the statistical properties and computational efficiency of SBR versus direct posterior sampling using spike-and-slab priors.
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors.
Horseshoe Regularization for Feature Subset Selection
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics.
A Statistical Theory of Deep Learning via Proximal Splitting
In this paper we develop a statistical theory and an implementation of deep learning models.
Proximal Algorithms in Statistics and Machine Learning
We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions.
Sampling Polya-Gamma random variates: alternate and approximate techniques
Efficiently sampling from the P\'olya-Gamma distribution, ${PG}(b, z)$, is an essential element of P\'olya-Gamma data augmentation.
Computation
Bayesian inference for logistic models using Polya-Gamma latent variables
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods.
