11 citations · 42 across the 8 of their papers we have counts for
9 papers
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Xun Qian, Rustem Islamov, Mher Safaryan +1
Recent advances in distributed optimization have shown that Newton-type methods with proper communication compression mechanisms can guarantee fast local rates and low communicatio…
Error Compensated Loopless SVRG, Quartz, and SDCA for Distributed Optimization
Xun Qian, Hanze Dong, Peter Richtárik +1
The communication of gradients is a key bottleneck in distributed training of large scale machine learning models. In order to reduce the communication cost, gradient compression (…
Distributed Second Order Methods with Fast Rates and Compressed Communication
Rustem Islamov, Xun Qian, Peter Richtárik
We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inher…
Error Compensated Distributed SGD Can Be Accelerated
Xun Qian, Peter Richtárik, Tong Zhang
Gradient compression is a recent and increasingly popular technique for reducing the communication cost in distributed training of large-scale machine learning models. In this work…
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Zhize Li, Dmitry Kovalev, Xun Qian +1
Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While…
L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik
We develop and analyze a new family of {\em nonaccelerated and accelerated loopless variance-reduced methods} for finite sum optimization problems. Our convergence analysis relies…