11 citations · 17 across the 14 of their papers we have counts for
14 papers
Distributed Online Convex Optimization with Nonseparable Costs and Constraints
Zhaoye Pan, Haozhe Lei, Fan Zuo +2
This paper studies distributed online convex optimization with time-varying coupled constraints, motivated by distributed online control in network systems. Most prior work assumes…
Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigation
Tao Li, Haozhe Lei, Mingsheng Yin +1
When using reinforcement learning (RL) to tackle physical control tasks, inductive biases that encode physics priors can help improve sample efficiency during training and enhance…
Traffic Adaptive Moving-window Service Patrolling for Real-time Incident Management during High-impact Events
Haozhe Lei, Ya-Ting Yang, Tao Li +4
This paper presents the Traffic Adaptive Moving-window Patrolling Algorithm (TAMPA), designed to improve real-time incident management during major events like sports tournaments a…
ADAPT: A Game-Theoretic and Neuro-Symbolic Framework for Automated Distributed Adaptive Penetration Testing
Haozhe Lei, Yunfei Ge, Quanyan Zhu
The integration of AI into modern critical infrastructure systems, such as healthcare, has introduced new vulnerabilities that can significantly impact workflow, efficiency, and sa…
PRADA: Proactive Risk Assessment and Mitigation of Misinformed Demand Attacks on Navigational Route Recommendations
Ya-Ting Yang, Haozhe Lei, Quanyan Zhu
Leveraging recent advances in wireless communication, IoT, and AI, intelligent transportation systems (ITS) played an important role in reducing traffic congestion and enhancing us…
Adaptive Incentive-Compatible Navigational Route Recommendations in Urban Transportation Networks
Ya-Ting Yang, Haozhe Lei, Quanyan Zhu
In urban transportation environments, drivers often encounter various path (route) options when navigating to their destinations. This emphasizes the importance of navigational rec…
Multi-level Traffic-Responsive Tilt Camera Surveillance through Predictive Correlated Online Learning
Tao Li, Zilin Bian, Haozhe Lei +5
In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of survei…
Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management
Tao Li, Zilin Bian, Haozhe Lei +6
Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road u…
Strategic Information Attacks on Incentive-Compatible Navigational Recommendations in Intelligent Transportation Systems
Ya-Ting Yang, Haozhe Lei, Quanyan Zhu
Intelligent transportation systems (ITS) have gained significant attention from various communities, driven by rapid advancements in informational technology. Within the realm of I…
Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environments
Haozhe Lei, Quanyan Zhu
In the area of learning-driven artificial intelligence advancement, the integration of machine learning (ML) into self-driving (SD) technology stands as an impressive engineering f…
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning
Tao Li, Haozhe Lei, Hao Guo +4
Millimeter-wave (mmWave) communication is a vital component of future generations of mobile networks, offering not only high data rates but also precise beams, making it ideal for…
Cognitive Level- Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving
Haozhe Lei, Quanyan Zhu
The potential market for modern self-driving cars is enormous, as they are developing remarkably rapidly. At the same time, however, accidents of pedestrian fatalities caused by au…
Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation
Tao Li, Haozhe Lei, Quanyan Zhu
Powered by deep representation learning, reinforcement learning (RL) provides an end-to-end learning framework capable of solving self-driving (SD) tasks without manual designs. Ho…
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysis
Tao Li, Haozhe Lei, Quanyan Zhu
Meta reinforcement learning (meta RL), as a combination of meta-learning ideas and reinforcement learning (RL), enables the agent to adapt to different tasks using a few samples. H…