5 papers
Total Variation Sparse Bayesian Learning for Block Sparsity via Majorization-Minimization
Yanbin He, Geethu Joseph
Block sparsity is a widely exploited structure in sparse recovery, offering significant gains when signal blocks are known. Yet, practical signals often exhibit unknown block bound…
Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling
Carlo Saccardi, Maximilian Pierzyna, Haitz Sáez de Ocáriz Borde +6
Kilometer-scale weather data is crucial for real-world applications but remains computationally intensive to produce using traditional weather simulations. An emerging solution is…
A Hierarchical View of Structured Sparsity in Kronecker Compressive Sensing
Yanbin He, Geethu Joseph
Kronecker compressed sensing refers to using Kronecker product matrices as sparsifying bases and measurement matrices in compressed sensing. This work focuses on the Kronecker comp…
Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals
Yanbin He, Geethu Joseph
This work studies the problem of jointly estimating unknown parameters from Kronecker-structured multidimensional signals, which arises in applications like intelligent reflecting…
Kronecker-structured Sparse Vector Recovery with Application to IRS-MIMO Channel Estimation
Yanbin He, Geethu Joseph
This paper studies the problem of Kronecker-structured sparse vector recovery from an underdetermined linear system with a Kronecker-structured dictionary. Such a problem arises in…