17 citations · 54 across the 14 of their papers we have counts for
14 papers
Evidential Deep Active Learning for Semi-Supervised Classification
Shenkai Zhao, Xinao Zhang, Lipeng Pan +2
Semi-supervised classification based on active learning has made significant progress, but the existing methods often ignore the uncertainty estimation (or reliability) of the pred…
Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
Shen-Yi Zhao, Chang-Wei Shi, Yin-Peng Xie +1
Stochastic gradient descent~(SGD) and its variants have been the dominating optimization methods in machine learning. Compared to SGD with small-batch training, SGD with large-batc…
Stagewise Enlargement of Batch Size for SGD-based Learning
Shen-Yi Zhao, Yin-Peng Xie, Wu-Jun Li
Existing research shows that the batch size can seriously affect the performance of stochastic gradient descent~(SGD) based learning, including training speed and generalization ab…
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…
Proximal SCOPE for Distributed Sparse Learning: Better Data Partition Implies Faster Convergence Rate
Shen-Yi Zhao, Gong-Duo Zhang, Ming-Wei Li +1
Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data…
Feature-Distributed SVRG for High-Dimensional Linear Classification
Gong-Duo Zhang, Shen-Yi Zhao, Hao Gao +1
Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to…
Lock-Free Optimization for Non-Convex Problems
Shen-Yi Zhao, Gong-Duo Zhang, Wu-Jun Li
Stochastic gradient descent~(SGD) and its variants have attracted much attention in machine learning due to their efficiency and effectiveness for optimization. To handle large-sca…
SCOPE: Scalable Composite Optimization for Learning on Spark
Shen-Yi Zhao, Ru Xiang, Ying-Hao Shi +2
Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed st…
Fast Asynchronous Parallel Stochastic Gradient Decent
Shen-Yi Zhao, Wu-Jun Li
Stochastic gradient descent~(SGD) and its variants have become more and more popular in machine learning due to their efficiency and effectiveness. To handle large-scale problems,…
Scalable Stochastic Alternating Direction Method of Multipliers
Shen-Yi Zhao, Wu-Jun Li, Zhi-Hua Zhou
Stochastic alternating direction method of multipliers (ADMM), which visits only one sample or a mini-batch of samples each time, has recently been proved to achieve better perform…