no code implementations • 21 Sep 2022 • Zhanhong Jiang, Aditya Balu, Xian Yeow Lee, Young M. Lee, Chinmay Hegde, Soumik Sarkar
To address these two issues, we propose a novel composite regret with a new network regret-based metric to evaluate distributed online optimization algorithms.
no code implementations • 28 Aug 2022 • Haoxiang Wang, Zhanhong Jiang, Chao Liu, Soumik Sarkar, Dongxiang Jiang, Young M. Lee
In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance.
1 code implementation • 6 Dec 2021 • Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu, Young M. Lee, Chinmay Hegde, Soumik Sarkar
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 over iterations.
Multi-agent Reinforcement Learning Policy Gradient Methods +3
no code implementations • 8 Nov 2019 • Zhanhong Jiang, Young M. Lee
This study proposes a deep supervised domain adaptation (DSDA) method for thermal dynamics modeling of building indoor temperature evolution and energy consumption.
no code implementations • 15 Oct 2019 • Zhanhong Jiang, Aditya Balu, Sin Yong Tan, Young M. Lee, Chinmay Hegde, Soumik Sarkar
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments.