Computing the product of the (binary) adjacency matrix of a large graph with a real-valued
vector is an important operation that lies at the heart of various graph analysis tasks, such as …

Matrix multiplication is an essential operation in the field of mathematics and computer
science. Many critical computations, such as matrix factorization and graph computations …

Computing the product of the (binary) adjacency matrix of a large graph with a real-valued
vector is an important operation that lies at the heart of various graph analysis tasks, such as …

Billion-scale Boolean matrices in the era of big data occupy storage that is measured in
100's of petabytes to zetabytes. The fundamental operation on these matrices for data …

We propose a new approach to large-scale machine learning, learning over compressed
data: First compress the training data somehow and then employ various machine learning …

We propose a general algorithm of constructing an extended formulation for any given set of
linear constraints with integer coefficients. Our algorithm consists of two phases: first …

A network can be represented as a graph. Most social networks like Facebook are
undirected, meaning that the relationship is mutual. We use the terminology reciprocity or …

Numerical optimization problems appear in many tasks of natural language processing
(NLP) and machine learning (ML), and have important roles in these areas. For example …

Online prediction is a theoretical framework of sequential decision making such as weather
forecasting and stock investment. The problem is formulated as a repeated game between …

Numerical optimization problems have an important role in natural language processing
(NLP) and many machine learning (ML) tasks, which are accomplished by first making a …