Recovery Guarantees

Learning Transition Operators From Sparse Space-Time Samples
We consider the nonlinear inverse problem of learning a transition operator A from partial observations at T different times, in the …
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Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from …
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A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares …
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Understanding and Enhancing Data Recovery Algorithms - From Noise-Blind Sparse Recovery to Reweighted Methods for Low-Rank Matrix Optimization
We prove new results about the robustness of noise-blind decoders for the problem of re- constructing a sparse vector from …
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