Low rank matrix completion (LRMC) has received tremendous
attention in recent years. The low rank assumption means that the
columns (or rows) of the matrix to be completed are points on a
low-dimensional linear variety. This work extends this...
Abstract: Low-rank matrices play a fundamental role in modeling
and computational methods for signal processing and machine
learning. In many applications where low-rank matrices arise, these
matrices cannot be fully sampled or directly observed...
Abstract: For many machine learning tasks, the input data lie on
a low-dimensional manifold embedded in a high-dimensional space
and, because of this high-dimensional structure, most algorithms
inefficient. The typical solution is to reduce the...
Abstract: We have seen the rise of high-dimensional omics data,
e.g., genome, transcriptome, microbiome, and proteome in recent
decades. The different types of missingness in modern omics data
bring up significant biological and statistical...
Abstract: Cells are the basic biological units of multicellular
organisms. The development of single-cell technologies such as
single cell RNA sequencing (scRNA-seq) have enabled us to study the
diversity of cell types in tissue and to elucidate the...
Abstract: I will give a brief introduction to the technology
followed by some exploratory data analysis demonstrating the
statistical challenges and how some of these can be considered
missing data problems.
Abstract: Recent advances in biotechnology and genomics have
generated dizzying amounts of large, noisy, and sparse datasets
that require concomitant development of machine learning methods.
The analyses of single-cell RNA-seq data have driven the...
Abstract: Representation learning is typically applied to only
one mode of a data matrix, either its rows or columns. Yet in many
applications, there is an underlying geometry to both the rows and
the columns. We propose utilizing this coupled...
Abstract: Causal inference has been widely conducted in various
fields and many methods have been proposed for different settings.
However, for noisy data with both mismeasurements and missing
observations, those methods often break down. In this...
