We describe a new software framework for fast training of generalized linear models. The
framework, named Snap Machine Learning (Snap ML), combines recent advances in …

Distributed machine learning (ML) has been extensively studied to meet the explosive
growth of training data. A wide range of machine learning models are trained by a family of …

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm
differentially private. Compared to the classical gradient descent algorithm where updates …

We propose a generic algorithmic building block to accelerate training of machine learning
models on heterogeneous compute systems. Our scheme allows to efficiently employ …

In this work we propose an asynchronous, GPU-based implementation of the widely-used
stochastic coordinate descent algorithm for convex optimization. We define the class of …

There is an increased interest in building data analytics frameworks with advanced
algebraic capabilities both in industry and academia. Many of these frameworks, eg …

This editorial is for the Special Issue of the journal Future Generation Computing Systems,
consisting of the selected papers of the 6th International Workshop on Parallel and …

In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm
with convergence guarantees that exhibits strong scalability. We start by studying a state-of …

We propose a generic algorithmic building block to accelerate training of machine learning
models on heterogeneous compute systems. Our scheme allows to efficiently employ …

The ever-growing number of edge devices (eg, smartphones) and the exploding volume of
sensitive data they produce, call for distributed machine learning techniques that are privacy …