8 citations · 25 across the 11 of their papers we have counts for
22 papers
Optimal convergence rates of totally asynchronous optimization
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information dela…
Delay-adaptive step-sizes for asynchronous learning
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchr…
A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation
Vien V. Mai, Jacob Lindbäck, Mikael Johansson
We develop a fast and reliable method for solving large-scale optimal transport (OT) problems at an unprecedented combination of speed and accuracy. Built on the celebrated Douglas…
Bandwidth-based Step-Sizes for Non-Convex Stochastic Optimization
Xiaoyu Wang, Mikael Johansson
Many popular learning-rate schedules for deep neural networks combine a decaying trend with local perturbations that attempt to escape saddle points and bad local minima. We derive…
A New Family of Feasible Methods for Distributed Resource Allocation
Xuyang Wu, Sindri Magnusson, Mikael Johansson
Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems…
On the Convergence of Step Decay Step-Size for Stochastic Optimization
Xiaoyu Wang, Sindri Magnússon, Mikael Johansson
The convergence of stochastic gradient descent is highly dependent on the step-size, especially on non-convex problems such as neural network training. Step decay step-size schedul…