1.7k citations · 2.1k across the 19 of their papers we have counts for
19 papers
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth +1
In this work, we consider the optimization formulation of personalized federated learning recently introduced by Hanzely and Richtárik (2020) which was shown to give an alternative…
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…
Stochastic Subspace Cubic Newton Method
Filip Hanzely, Nikita Doikov, Peter Richtárik +1
In this paper, we propose a new randomized second-order optimization algorithm---Stochastic Subspace Cubic Newton (SSCN)---for minimizing a high dimensional convex function . Ou…
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
Samuel Horváth, Lihua Lei, Peter Richtárik +1
Adaptivity is an important yet under-studied property in modern optimization theory. The gap between the state-of-the-art theory and the current practice is striking in that algori…
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…