185 citations · 792 across the 41 of their papers we have counts for
4 papers · 2 filters
Efficient Greedy Coordinate Descent for Composite Problems
Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich +1
Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate…
Local SGD Converges Fast and Communicates Little
Sebastian U. Stich
Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed training. The scheme can reach a linear speedup with respect to the number of workers, b…
k-SVRG: Variance Reduction for Large Scale Optimization
Anant Raj, Sebastian U. Stich
Variance reduced stochastic gradient (SGD) methods converge significantly faster than the vanilla SGD counterpart. However, these methods are not very practical on large scale prob…
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization
Robert M. Gower, Filip Hanzely, Peter Richtárik +1
We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particu…