1 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
Distributed Online Non-convex Optimization with Composite Regret
Zhanhong Jiang, Aditya Balu, Xian Yeow Lee +3
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model va…
Asynchronous Training Schemes in Distributed Learning with Time Delay
Haoxiang Wang, Zhanhong Jiang, Chao Liu +3
In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance. This issue is usually tackled by delay tolerant a…
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan +5
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches ove…
Decentralized Deep Learning using Momentum-Accelerated Consensus
Aditya Balu, Zhanhong Jiang, Sin Yong Tan +3
We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While there exist several decentralized deep learning…
On Higher-order Moments in Adam
Zhanhong Jiang, Aditya Balu, Sin Yong Tan +3
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based…