A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

We present a novel binary convex reformulation of the sparse regression problem that
constitutes a new duality perspective. We devise a new cutting plane method and provide …

In many application areas ranging from bioinformatics to imaging, we are faced with the
following question: can we recover a sparse vector xo∈ ℝ N from its undersampled set of …

We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …

What latent features are encoded in language model (LM) representations? Recent work on
training sparse autoencoders (SAEs) to disentangle interpretable features in LM …

Recovering sparse vectors and low-rank matrices from noisy linear measurements has been
the focus of much recent research. Various reconstruction algorithms have been studied …

In this paper, we study the theoretical properties of iteratively re-weighted least squares
(IRLS) algorithms and their utility in sparse signal recovery in the presence of noise. We …

The increasing number of Internet of Things (IoT) objects has been a growing challenge of
the current spectrum supply. To handle this issue, the IoT devices should have cognitive …

In inverse synthetic aperture radar (ISAR) imaging, a target is usually regarded as consist of
a few strong (specular) scatterers and the distribution of these strong scatterers is sparse in …