10 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 10 cited
Federated Reinforcement Learning with Environment Heterogeneity
Hao Jin, Yang Peng, Wenhao Yang +2
We study a Federated Reinforcement Learning (FedRL) problem in which agents collaboratively learn a single policy without sharing the trajectories they collected during agent-e…
cs.LG2021
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
Guangzeng Xie, Hao Jin, Dachao Lin +1
We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods. Our approach modifies the objective…
stat.ML2019
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks
Hao Jin, Dachao Lin, Zhihua Zhang
Stochastic variance-reduced gradient (SVRG) is a classical optimization method. Although it is theoretically proved to have better convergence performance than stochastic gradient…