3k citations · 7.9k across the 23 of their papers we have counts for
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Distributed Optimization with Arbitrary Local Solvers
Chenxin Ma, Jakub Konečný, Martin Jaggi +4
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. Rece…
Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konečný, Brendan McMahan, Daniel Ramage
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are distributed (unevenly) over an e…
Stop Wasting My Gradients: Practical SVRG
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani +3
We present and analyze several strategies for improving the performance of stochastic variance-reduced gradient (SVRG) methods. We first show that the convergence rate of these met…
Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Jakub Konečný, Jie Liu, Peter Richtárik +1
We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We co…