9 citations · 18 across the 8 of their papers we have counts for
6 papers · 1 filter
Neighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
Molly Noel, Gabriel Mancino-Ball, Yangyang Xu
Graph convolutional networks (GCNs) are a powerful tool for graph representation learning. Due to the recursive neighborhood aggregations employed by GCNs, efficient training metho…
A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Wei Liu, Muhammad Khan, Gabriel Mancino-Ball +1
We study a class of stochastic nonsmooth optimization problems in which an outer variable minimizes the expectation of a pointwise maximum. This minimization--expectation--maximiza…
Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems
Gabriel Mancino-Ball, Yangyang Xu, Muhammad Khan
In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wh…
Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization
Xuan Zhang, Gabriel Mancino-Ball, Necdet Serhat Aybat +1
We propose a novel single-loop decentralized algorithm called DGDA-VR for solving the stochastic nonconvex strongly-concave minimax problem over a connected network of agents.…
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu +1
Consider a network of decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called…
A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization
Gabriel Mancino-Ball, Yangyang Xu, Jie Chen
In this work, we introduce ADAPD, ecentrlized rimal-ual algorithmic framework for solving non-convex and smooth consens…