most citedMulti-Agent Learning for Resilient Distributed Control Systems

1 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2024

Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning

Yuhan Zhao, Lan Shi, Quanyan Zhu

As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapti…

cs.RO2024

Stackelberg Meta-Learning Based Shared Control for Assistive Driving

Yuhan Zhao, Quanyan Zhu

Shared control allows the human driver to collaborate with an assistive driving system while retaining the ability to make decisions and take control if necessary. However, human-v…

cs.IR202325 cited

Augmented Negative Sampling for Collaborative Filtering

Yuhan Zhao, Rui Chen, Riwei Lai +3

Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised lea…

cs.CV20231 cited

GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning

Huanzhang Dou, Pengyi Zhang, Yuhan Zhao +3

Gait recognition aims at identifying the pedestrians at a long distance by their biometric gait patterns. It is inherently challenging due to the various covariates and the propert…

eess.SY20221 cited

Multi-Agent Learning for Resilient Distributed Control Systems

Yuhan Zhao, Craig Rieger, Quanyan Zhu

Resilience describes a system's ability to function under disturbances and threats. Many critical infrastructures, including smart grids and transportation networks, are large-scal…