73 citations · 162 across the 20 of their papers we have counts for
12 papers · 1 filter
DFWLayer: Differentiable Frank-Wolfe Optimization Layer
Zixuan Liu, Liu Liu, Xueqian Wang +1
Differentiable optimization has received a significant amount of attention due to its foundational role in the domain of machine learning based on neural networks. This paper propo…
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Yifan Shi, Yingqi Liu, Yan Sun +4
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. Howe…
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yifan Shi, Kang Wei, Li Shen +4
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
Guozheng Ma, Linrui Zhang, Haoyu Wang +6
Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transforma…
Make Landscape Flatter in Differentially Private Federated Learning
Yifan Shi, Yingqi Liu, Kang Wei +3
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…
Improving the Model Consistency of Decentralized Federated Learning
Yifan Shi, Li Shen, Kang Wei +4
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its…
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Qin Zhang, Linrui Zhang, Haoran Xu +6
Offline safe RL is of great practical relevance for deploying agents in real-world applications. However, acquiring constraint-satisfying policies from the fixed dataset is non-tri…
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan +4
Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust represe…
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning
Linrui Zhang, Zichen Yan, Li Shen +3
Learning a risk-aware policy is essential but rather challenging in unstructured robotic tasks. Safe reinforcement learning methods open up new possibilities to tackle this problem…
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Linrui Zhang, Qin Zhang, Li Shen +3
Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, the…
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
Linrui Zhang, Qin Zhang, Li Shen +2
Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well. Considering the distinctiveness of t…
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Linrui Zhang, Li Shen, Long Yang +4
Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still s…