1 citations · 1 across the 3 of their papers we have counts for
5 papers
Preserving Structure in Variational Data Assimilation of Hyperbolic Conservation Laws via Regularization
Yao Xiao, Anne Gelb
Hyperbolic conservation laws pose a challenging setting for variational data assimilation, since the Gaussian assumption imposes a smoothness that smears jump discontinuities. A sp…
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…
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…
Complex-Valued Signal Recovery using the Bayesian LASSO
Dylan Green, Jonathan Lindbloom, Anne Gelb
Recovering complex-valued image recovery from noisy indirect data is important in applications such as ultrasound imaging and synthetic aperture radar. While there are many effecti…
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…