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Found 6 papers, 1 papers with code
Corrupted Sensing: Novel Guarantees for Separating Structured Signals
While an arbitrary signal cannot be recovered in the face of arbitrary corruption, tractable recovery is possible when both signal and corruption are suitably structured.
Matrix reconstruction with the local max norm
We introduce a new family of matrix norms, the ''local max'' norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed weighted trace norms, which have been extensively used in the literature as regularizers for matrix reconstruction problems.
Sparse Prediction with the k-Support Norm
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an $\ell_2$ penalty.
Nonparametric Reduced Rank Regression
We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models.
Learning with the weighted trace-norm under arbitrary sampling distributions
We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions.
Extended Bayesian Information Criteria for Gaussian Graphical Models
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications.
Statistics Theory Statistics Theory
