Solving computationally hard problems using conventional computing architectures is often
slow and energetically inefficient. Quantum computing may help with these challenges, but it …

Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stakes
issue. Vanilla Cholesky samplers imply a computational cost and memory requirements that …

Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in
machine learning and other domains. Since this is likely to continue for the foreseeable …

We perform the first study of the tradeoff space of access methods and replication to support
statistical analytics using first-order methods executed in the main memory of a Non-Uniform …

Datasets are growing not just in size but in complexity, creating a demand for rich models
and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but …

In the age of Big Data, efficient algorithms are now in higher demand more than ever before.
While Big Data takes us into the asymptotic world envisioned by our pioneers, it also …

Efficiently aggregating data from different sources is a challenging problem, particularly
when samples from each source are distributed differently. These differences can be …

Many pressing questions in science are macroscopic: they require scientists to consult
information expressed in a wide range of resources, many of which are not organized in a …

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and
Multi-Target Tracking (MTT) are important in many applications. In this work, we address the …

The explosive growth in data volume and the availability of cheap computing resources
have sparked increasing interest in Big learning, an emerging subfield that studies scalable …