Statistical methods for genome-wide association studies (GWAS) continue to improve.
However, the increasing volume and variety of genetic and genomic data make …

Iterative thresholding algorithms seek to optimize a differentiable objective function over a
sparsity or rank constraint by alternating between gradient steps that reduce the objective …

Stochastic gradient hard thresholding methods have recently been shown to work favorably
in solving large-scale empirical risk minimization problems under sparsity or rank constraint …

Sparse signal recovery or compressed sensing can be formulated as certain sparse
optimization problems. The classic optimization theory indicates that the Newton-like method …

The hard thresholding technique plays a vital role in the development of algorithms for
sparse signal recovery. By merging this technique and heavy-ball acceleration method …

Bayesian coresets have emerged as a promising approach for implementing scalable
Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of …

We accelerate the iterative hard thresholding (IHT) method, which finds (k) important
elements from a parameter vector in a linear regression model. Although the plain IHT …

The simulations indicate that the existing hard thresholding technique independent of the
residual function may cause a dramatic increase or numerical oscillation of the residual. This …

Many recent problems in signal processing and machine learning such as compressed
sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can …

The optimal k-thresholding (OT) and optimal k-thresholding pursuit (OTP) are newly
introduced frameworks of thresholding techniques for compressed sensing and signal …