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Sparse Regularization in Marketing and Economics
Guanhao Feng, Nicholas Polson, Yuexi Wang +1
Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution.…
Deep Learning: A Bayesian Perspective
Nicholas Polson, Vadim Sokolov
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of i…
Bayesian -regularized Least Squares
Nicholas G. Polson, Lei Sun
Bayesian -regularized least squares is a variable selection technique for high dimensional predictors. The challenge is optimizing a non-convex objective function via search o…
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Matthew F. Dixon, Nicholas G. Polson, Vadim O. Sokolov
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for s…
Horseshoe Regularization for Feature Subset Selection
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
Feature subset selection arises in many high-dimensional applications of statistics, such as compressed sensing and genomics. The penalty is ideal for this task, the cavea…