Rui Zhao

Latest

• CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
• X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
• Spatial-Temporal Large Language Model for Traffic Prediction
• TimeCMA: Towards LLM-Empowered Time Series Forecasting via Cross-Modality Alignment
• VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network
• SQL-to-Schema Enhances Schema Linking in Text-to-SQL
• PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
• DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge
• Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning
• MultiFun-DAG: Multivariate Functional Directed Acyclic Graph
• TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
• Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
• PET-SQL: A Prompt-enhanced Two-stage Text-to-SQL Framework with Cross-consistency
• SQLBench: Benchmarking the Text-to-SQL Capability of Large Language Models - A Comprehensive Evaluation
• A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
• KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
• Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
• TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
• Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
• Relation-Aware Distribution Representation Network for Person Clustering With Multiple Modalities
• MM-DAG: Multi-task DAG Learning for Multi-modal Data--with Application for Traffic Congestion Analysis
• GESA: A General Scenario-agnostic Reinforcement Learning for Traffic Signal Control
• Jointly contrastive representation learning on road network and trajectory