242 citations · 1.2k across the 37 of their papers we have counts for
11 papers · 1 filter
CADA: Communication-Adaptive Distributed Adam
Tianyi Chen, Ziye Guo, Yuejiao Sun +1
Stochastic gradient descent (SGD) has taken the stage as the primary workhorse for large-scale machine learning. It is often used with its adaptive variants such as AdaGrad, Adam,…
Hybrid Federated Learning: Algorithms and Implementation
Xinwei Zhang, Wotao Yin, Mingyi Hong +1
Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is ofte…
Neural Network Compression Via Sparse Optimization
Tianyi Chen, Bo Ji, Yixin Shi +4
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have…
Half-Space Proximal Stochastic Gradient Method for Group-Sparsity Regularized Problem
Tianyi Chen, Guanyi Wang, Tianyu Ding +3
Optimizing with group sparsity is significant in enhancing model interpretability in machining learning applications, e.g., feature selection, compressed sensing and model compress…
Byzantine-Resilient Decentralized TD Learning with Linear Function Approximation
Zhaoxian Wu, Han Shen, Tianyi Chen +1
This paper considers the policy evaluation problem in a multi-agent reinforcement learning (MARL) environment over decentralized and directed networks. The focus is on decentralize…
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Tianyi Chen, Yuejiao Sun, Wotao Yin
Stochastic compositional optimization generalizes classic (non-compositional) stochastic optimization to the minimization of compositions of functions. Each composition may introdu…