With the growth of data and necessity for distributed optimization methods, solvers that work
well on a single machine must be re-designed to leverage distributed computation. Recent …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

Distributed optimization methods for large-scale machine learning suffer from a
communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and …

Random projections offer an appealing and flexible approach to a wide range of large‐scale
statistical problems. They are particularly useful in high‐dimensional settings, where we …

Fitting linear regression models can be computationally very expensive in large-scale data
analysis tasks if the sample size and the number of variables are very large. Random …

We study optimization algorithms for the finite sum problems frequently arising in machine
learning applications. First, we propose novel variants of stochastic gradient descent with a …

Sketching techniques scale up machine learning algorithms by reducing the sample size or
dimensionality of massive data sets, without sacrificing their statistical properties. In this …

We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical
estimation. DUAL-LOCO assumes that the data is distributed across workers according to …

Learning an effective global model on private and decentralized datasets has become an
increasingly important challenge of machine learning when applied in practice. Existing …

In high-dimensional data modeling, variable selection plays a crucial role in improving
predictive accuracy and enhancing model interpretability through sparse representation …