1 code implementation • NeurIPS 2019 • Siyuan Li, Rui Wang, Minxue Tang, Chongjie Zhang
In addition, we also theoretically prove that optimizing low-level skills with this auxiliary reward will increase the task return for the joint policy.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • 20 Apr 2020 • Huanrui Yang, Minxue Tang, Wei Wen, Feng Yan, Daniel Hu, Ang Li, Hai Li, Yiran Chen
In this work, we propose SVD training, the first method to explicitly achieve low-rank DNNs during training without applying SVD on every step.
no code implementations • CVPR 2022 • Minxue Tang, Xuefei Ning, Yitu Wang, Jingwei Sun, Yu Wang, Hai Li, Yiran Chen
In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
no code implementations • 30 Mar 2022 • Jingyu Pan, Chen-Chia Chang, Zhiyao Xie, Ang Li, Minxue Tang, Tunhou Zhang, Jiang Hu, Yiran Chen
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
no code implementations • 8 Sep 2022 • Minxue Tang, Jianyi Zhang, Mingyuan Ma, Louis DiValentin, Aolin Ding, Amin Hassanzadeh, Hai Li, Yiran Chen
However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices.
no code implementations • 30 Sep 2022 • Jianyi Zhang, Ang Li, Minxue Tang, Jingwei Sun, Xiang Chen, Fan Zhang, Changyou Chen, Yiran Chen, Hai Li
Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees.