55 citations · 484 across the 40 of their papers we have counts for
7 papers · 1 filter
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
Cheng Chen, Junjie Yang, Yi Zhou
Modern deep neural network (DNN) trainings utilize various training techniques, e.g., nonlinear activation functions, batch normalization, skip-connections, etc. Despite their effe…
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis
Shaocong Ma, Yi Zhou, Shaofeng Zou
Variance reduction techniques have been successfully applied to temporal-difference (TD) learning and help to improve the sample complexity in policy evaluation. However, the exist…
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
Cheng Chen, Ziyi Chen, Yi Zhou +1
We develop FedCluster--a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties. The FedCluster groups the…
Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle
Shaocong Ma, Yi Zhou
Although SGD with random reshuffle has been widely-used in machine learning applications, there is a limited understanding of how model characteristics affect the convergence of th…
Momentum with Variance Reduction for Nonconvex Composition Optimization
Ziyi Chen, Yi Zhou
Composition optimization is widely-applied in nonconvex machine learning. Various advanced stochastic algorithms that adopt momentum and variance reduction techniques have been dev…
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Yi Zhou, Zhe Wang, Kaiyi Ji +2
Various types of parameter restart schemes have been proposed for accelerated gradient algorithms to facilitate their practical convergence in convex optimization. However, the con…