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 consider identification and estimation with an outcome missing not at random (MNAR).
We study an identification strategy based on a so-called shadow variable. A shadow …

This article investigates statistical inference for noisy matrix completion in a semi-supervised
model when auxiliary covariates are available. The model consists of two parts. One part is a …

We study the problem of symmetric positive semi-definite low-rank matrix completion (MC)
with deterministic entry-dependent sampling. In particular, we consider rectified linear unit …

Noisy matrix completion has attracted significant attention due to its applications in
recommendation systems, signal processing and image restoration. Most existing works rely …

We study the problem of missing not at random (MNAR) datasets with binary outcomes. We
propose an exponential tilt based approach that bypasses any knowledge on'nonresponse …

Modern machine learning tasks often involve the training of over-parameterized models and
the challenge of addressing data bias. However, despite recent advances, there remains a …

While several recent matrix completion methods are developed to deal with non-uniform
observation probabilities across matrix entries, very few allow the missingness to depend on …