arXiv Machine Learning
From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 22
Group Distributionally Robust Optimization over Decentralized Networks
arXiv:2610. 29261v1 Announce Type: cross Abstract: We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk.
arXiv AI
Aug 12
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
arXiv Statistics ML
Sep 15
Distributed Fast Fixed-Point Algorithms for Composite Monotone Inclusions over Networks
arXiv:2609.14953v1 Announce Type: cross Abstract: This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, $0 \in \sum_{i=1}^n (G_ix + T_ix)$, o...
Hugging Face Trending Papers
Oct 21
Group Distributionally Robust Optimization over Decentralized Networks
We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk. Existing methods optimize...
arXiv Machine Learning
Jul 15
Decentralized Gradient Descent: Bottleneck Regimes and Budget Complexity
arXiv:2607. 12172v1 Announce Type: cross Abstract: Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents.
arXiv Machine Learning
Jun 8
Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization
arXiv:2606. 07496v1 Announce Type: new Abstract: Decentralized stochastic optimization is a fundamental paradigm for large-scale learning over networks, where agents communicate only with their neighbors and no central coordinator is required.