29 citations · 49 across the 6 of their papers we have counts for
6 papers
On the improved conditions for some primal-dual algorithms
Yao Li, Ming Yan
The convex minimization of over with differentiable and linear operator $\mathbf{A}: \mathbb{R}^n\rightarro…
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…
ErrorCompensatedX: error compensation for variance reduced algorithms
Hanlin Tang, Yao Li, Ji Liu +1
Communication cost is one major bottleneck for the scalability for distributed learning. One approach to reduce the communication cost is to compress the gradient during communicat…
Linear Convergent Decentralized Optimization with Compression
Xiaorui Liu, Yao Li, Rongrong Wang +2
Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DG…
A Double Residual Compression Algorithm for Efficient Distributed Learning
Xiaorui Liu, Yao Li, Jiliang Tang +1
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms. However, the communication cost of gradient aggregation and model synchron…
On linear convergence of two decentralized algorithms
Yao Li, Ming Yan
Decentralized algorithms solve multi-agent problems over a connected network, where the information can only be exchanged with the accessible neighbors. Though there exist several…