1 citations · 1 across the 4 of their papers we have counts for
4 papers
ADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
Chenliang Li, Junyu Leng, Jiaxiang Li +4
Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which i…
Local Linear Convergence of Infeasible Optimization with Orthogonal Constraints
Youbang Sun, Shixiang Chen, Alfredo Garcia +1
Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradi…
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Hao Sun, Li Shen, Shixiang Chen +4
Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data. The time cost of comm…
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Hao Sun, Li Shen, Qihuang Zhong +6
Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize better for training deep neural networks via introducing extra perturbation steps to…