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:2606. 04757v1 Announce Type: cross Abstract: We study decentralized stochastic smooth convex optimization, where $M$ workers minimize an average objective using local stochastic gradients and neighbor-only communication over a fixed gossip network.
By Nitai Kluger, Amit Attia, Tomer Koren
arXiv:2610. 10527v1 Announce Type: cross Abstract: Heavy-tailed noise has been widely observed in modern machine learning, motivating the use of methods like gradient clipping and normalization.
By Aleksandar Armacki, Haoyuan Cai, Ali H. Sayed
The paper introduces Dec-BFTRL, a decentralized algorithm for online optimization of upper-linearizable payoffs with efficient separation access, targeting continuous diminishing-return submodular maximization. Each agent evaluates its action against the average of local objectives, projects via an approximate gauge, exchanges a cumulative surrogate-gradient dual state, and uses a local HybridNewton step to minimize its BFTRL potential. The method achieves an expected network-aggregate regret of “~O(√T)” while requiring T neighbor-mixing steps and ~O(T) separation-oracle calls per agent, and provides four wrapper instantiations for three DR-submodular problems.
By Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal
We study decentralized online optimization of upper-linearizable payoffs over an action set under efficient separation access, with applications to online continuous diminishing-return (DR) submodular...
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.
By Ming Sun, Kun Yuan