We consider the problem of personalized federated learning when there ar...
We study the optimization aspects of personalized Federated Learning (FL...
Large scale distributed optimization has become the default tool for the...
We present a unified framework for analyzing local SGD methods in the co...
In this work, we consider the optimization formulation of personalized
f...
Many key problems in machine learning and data science are routinely mod...
In this paper, we propose a new randomized second-order optimization
alg...
We propose an accelerated version of stochastic variance reduced coordin...
We propose a new optimization formulation for training federated learnin...
We propose a remarkably general variance-reduced method suitable for sol...
In this paper we introduce a unified analysis of a large family of varia...
The best pair problem aims to find a pair of points that minimize the
di...
It is well known that many optimization methods, including SGD, SAGA, an...
In this work we present a randomized gossip algorithm for solving the av...
We propose a randomized first order optimization method--SEGA (SkEtched
...
Robust principal component analysis (RPCA) is a well-studied problem wit...
Relative smoothness - a notion introduced by Birnbaum et al. (2011) and
...
We present the first accelerated randomized algorithm for solving linear...