81 citations · 116 across the 5 of their papers we have counts for
5 papers
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely, Dmitry Kovalev, Peter Richtarik
We propose an accelerated version of stochastic variance reduced coordinate descent -- ASVRCD. As other variance reduced coordinate descent methods such as SEGA or SVRCD, our metho…
Distributed Fixed Point Methods with Compressed Iterates
Sélim Chraibi, Ahmed Khaled, Dmitry Kovalev +3
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated…
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates
Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik
We present two new remarkably simple stochastic second-order methods for minimizing the average of a very large number of sufficiently smooth and strongly convex functions. The fir…
Stochastic Distributed Learning with Gradient Quantization and Variance Reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko +2
We consider distributed optimization where the objective function is spread among different devices, each sending incremental model updates to a central server. To alleviate the co…
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
Dmitry Kovalev, Samuel Horvath, Peter Richtarik
The stochastic variance-reduced gradient method (SVRG) and its accelerated variant (Katyusha) have attracted enormous attention in the machine learning community in the last few ye…