5 citations · 19 across the 7 of their papers we have counts for
18 papers
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks
Xinmeng Huang, Kun Yuan
Decentralized optimization with time-varying networks is an emerging paradigm in machine learning. It saves remarkable communication overhead in large-scale deep training and is mo…
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Kun Yuan, Xinmeng Huang, Yiming Chen +3
Decentralized optimization is effective to save communication in large-scale machine learning. Although numerous algorithms have been proposed with theoretical guarantees and empir…
On the Performance of Gradient Tracking with Local Updates
Edward Duc Hien Nguyen, Sulaiman A. Alghunaim, Kun Yuan +1
We study the decentralized optimization problem where a network of agents seeks to minimize the average of a set of heterogeneous non-convex cost functions distributedly. State…
Exponential Graph is Provably Efficient for Decentralized Deep Training
Bicheng Ying, Kun Yuan, Yiming Chen +3
Decentralized SGD is an emerging training method for deep learning known for its much less (thus faster) communication per iteration, which relaxes the averaging step in parallel S…
Decentralized Composite Optimization with Compression
Yao Li, Xiaorui Liu, Jiliang Tang +2
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottlenec…
Accelerating Gossip SGD with Periodic Global Averaging
Yiming Chen, Kun Yuan, Yingya Zhang +3
Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient th…