17 citations · 54 across the 14 of their papers we have counts for
5 papers · 1 filter
Clustered Reinforcement Learning
Xiao Ma, Shen-Yi Zhao, Wu-Jun Li
Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. Durin…
ADASS: Adaptive Sample Selection for Training Acceleration
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Stochastic gradient decent~(SGD) and its variants, including some accelerated variants, have become popular for training in machine learning. However, in all existing SGD and its v…
On the Convergence of Memory-Based Distributed SGD
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Distributed stochastic gradient descent~(DSGD) has been widely used for optimizing large-scale machine learning models, including both convex and non-convex models. With the rapid…
Global Momentum Compression for Sparse Communication in Distributed Learning
Chang-Wei Shi, Shen-Yi Zhao, Yin-Peng Xie +2
With the rapid growth of data, distributed momentum stochastic gradient descent~(DMSGD) has been widely used in distributed learning, especially for training large-scale deep model…
Quantized Epoch-SGD for Communication-Efficient Distributed Learning
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Due to its efficiency and ease to implement, stochastic gradient descent (SGD) has been widely used in machine learning. In particular, SGD is one of the most popular optimization…