3 papers
math.NA2025
Joint Signal Recovery and Uncertainty Quantification via the Residual Prior Transform
Yao Xiao, Anne Gelb
Conventional priors used for signal recovery are often limited by the assumption that the type of a signal's variability, such as piecewise constant or linear behavior, is known an…
math.NA2025
A new sparsity promoting residual transform operator for Lasso regression
Yao Xiao, Anne Gelb, Aditya Viswanathan
Lasso regression is a widely employed approach within the regularization framework used to promote sparsity and recover piecewise smooth signals $f:[a,b) \rightarrow \math…
math.NA2025
Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Jonathan Lindbloom, Jan Glaubitz, Anne Gelb
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian pri…