Adcock, Ben

Adaptive sampling strategies for function approximation in high dimensions

Breaking the Coherence Barrier: A New Theory for Compressed Sensing

Compressive imaging with total variation regularization and application to auto-calibration of parallel magnetic resonance imaging

Global guarantees from local knowledge: Stable and robust recovery of sparse in levels vectors

Lagrangian duality and Adiabatic quantum computation for constrained optimization problems

Unrolled NESTA: Constructing stable, accurate and efficient neural networks for gradient-sparse imaging problems

Weighted l1 minimization techniques for compressed sensing and their applications