81 citations · 181 across the 12 of their papers we have counts for
4 papers · 1 filter
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov, Adrien Taylor, Samuel Horváth +1
Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follo…
Adaptive Learning Rates for Faster Stochastic Gradient Methods
Samuel Horváth, Konstantin Mishchenko, Peter Richtárik
In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Po…
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity
Samuel Horváth
Federated learning (FL) is an emerging machine learning paradigm involving multiple clients, e.g., mobile phone devices, with an incentive to collaborate in solving a machine learn…
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth, Maziar Sanjabi, Lin Xiao +2
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck i…