1k citations · 1.6k across the 56 of their papers we have counts for
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Statistical sparsity
Peter McCullagh, Nicholas Polson
The main contribution of this paper is a mathematical definition of statistical sparsity, which is expressed as a limiting property of a sequence of probability distributions. The…
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.…
On Hilbert's 8th Problem
Nicholas G. Polson
This note takes the probabilistic half of the Riemann Xi story on its own terms. Every object in the subject is a first passage time: Riemann's kernel is the law of the logarithm o…
Lasso Meets Horseshoe : A Survey
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. La…
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…