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Found 7 papers, 1 papers with code
Nonlinear Distributionally Robust Optimization
Motivated by this, we propose an alternative notion for the derivative and corresponding smoothness based on Gateaux (G)-derivative for generic risk measures.
Fast Algorithm for Constrained Linear Inverse Problems
We consider the constrained Linear Inverse Problem (LIP), where a certain atomic norm (like the $\ell_1 $ norm) is minimized subject to a quadratic constraint.
Novel min-max reformulations of Linear Inverse Problems
In this article, we dwell into the class of so-called ill-posed Linear Inverse Problems (LIP) which simply refers to the task of recovering the entire signal from its relatively few random linear measurements.
Dictionary Learning with Almost Sure Error Constraints
A dictionary is a database of standard vectors, so that other vectors / signals are expressed as linear combinations of dictionary vectors, and the task of learning a dictionary for a given data is to find a good dictionary so that the representation of data points has desirable features.
On Convex Duality in Linear Inverse Problems
In this article we dwell into the class of so called ill posed Linear Inverse Problems (LIP) in machine learning, which has become almost a classic in recent times.
A complete characterization of optimal dictionaries for least squares representation
Dictionaries are collections of vectors used for representations of elements in Euclidean spaces.
Optimal dictionary for least squares representation
Dictionaries are collections of vectors used for representations of random vectors in Euclidean spaces.
