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Found 8 papers, 5 papers with code
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
We prove locally quadratic convergence of the iterates to a simultaneously structured data matrix in a regime of minimal sample complexity (up to constants and a logarithmic factor), which is known to be impossible for a combination of convex surrogates.
On the Convergence of IRLS and Its Variants in Outlier-Robust Estimation
Outlier-robust estimation involves estimating some parameters (e. g., 3D rotations) from data samples in the presence of outliers, and is typically formulated as a non-convex and non-smooth problem.
Learning Transition Operators From Sparse Space-Time Samples
This Spatio-Temporal Transition Operator Recovery problem is motivated by the recent interest in learning time-varying graph signals that are driven by graph operators depending on the underlying graph topology.
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 (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate.
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
The recovery of sparse data is at the core of many applications in machine learning and signal processing.
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoothing Newton method applied to a non-convex rank surrogate objective.
The Oracle of DLphi
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results.
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
We propose a new iteratively reweighted least squares (IRLS) algorithm for the recovery of a matrix $X \in \mathbb{C}^{d_1\times d_2}$ of rank $r \ll\min(d_1, d_2)$ from incomplete linear observations, solving a sequence of low complexity linear problems.
Numerical Analysis Information Theory Information Theory Optimization and Control
