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Found 6 papers, 1 papers with code
Merging Two Cultures: Deep and Statistical Learning
We show that prediction, interpolation and uncertainty quantification can be achieved using probabilistic methods at the output layer of the model.
On Posterior consistency of Bayesian Changepoint models
We consider a hierarchical Bayesian linear model where the active set of covariates that affects the observations through a mean model can vary between different time segments.
Model Selection
Variable Selection
Methodology
Statistics Theory
Statistics Theory
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold.
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.
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
Horseshoe Regularization for Feature Subset Selection
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics.
