Publications de Chenxin Ma | EPFL Graph Search

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Distributed optimization algorithms are essential for training machine learning models on very large-scale datasets. However, they often suffer from communication bottlenecks. Confronting this issue, a communication-efficient primal-dual coordinate ascent ...

2021

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Martin Jaggi, Chenxin Ma

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme a ...

2018

Distributed Optimization with Arbitrary Local Solvers

Martin Jaggi, Chenxin Ma

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 work in this area has been limited by focusing heavily on developing high ...

Taylor & Francis Ltd2017

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Martin Jaggi, Chenxin Ma

2018