Yinuo Zhao

Orcid: 0009-0000-9674-7421

According to our database1, Yinuo Zhao authored at least 27 papers between 2020 and 2026.

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Timeline

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Bibliography

2026
RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation.
CoRR, February, 2026

Performance analysis of covert communication in the presence of intelligent eavesdroppers.
Phys. Commun., 2026

On the physical layer security of CR-NOMA assisted intelligent transportation networks.
Int. J. Sens. Networks, 2026

When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being.
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 2026

2025
Real-world Reinforcement Learning from Suboptimal Interventions.
CoRR, December, 2025

Reliability and security analysis of NOMA-UAV communication network in intelligent eavesdropping environment.
Telecommun. Syst., September, 2025

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning.
IEEE Trans. Neural Networks Learn. Syst., June, 2025

ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning.
CoRR, June, 2025

Explicitly Integrated Multitask Learning in a Hybrid Network for Remote Sensing Road Extraction.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025

Y-Net-ECG: A Multi-Lead informed and interpretable architecture for ECG segmentation across diverse rhythms.
Expert Syst. Appl., 2025

RoboAfford: A Dataset and Benchmark for Enhancing Object and Spatial Affordance Learning in Robot Manipulation.
Proceedings of the 33rd ACM International Conference on Multimedia, 2025

HACTS: a Human-As-Copilot Teleoperation System for Robot Learning.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025

Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation.
Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, 2025

Training-Free Generation of Temporally Consistent Rewards from VLMs.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

2024
Energy-Efficient Ground-Air-Space Vehicular Crowdsensing by Hierarchical Multi-Agent Deep Reinforcement Learning With Diffusion Models.
IEEE J. Sel. Areas Commun., December, 2024

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning.
CoRR, 2024

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation.
CoRR, 2024

An Efficient Generalizable Framework for Visuomotor Policies via Control-aware Augmentation and Privilege-guided Distillation.
CoRR, 2024

2023
LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction.
Decis. Support Syst., June, 2023

Intelligent Multi-peak Beam Training in mmWave Communications with Deep Neural Networks.
Proceedings of the International Conference on Wireless Communications and Signal Processing, 2023

Multi-Skill Policy Transfer by Option-based Deep Reinforcement Learning for Autonomous Driving.
Proceedings of the 9th International Conference on Big Data Computing and Communications, 2023

2022
Chinese New Media Stocks Liquidity Risk And Spillover Effect.
Proceedings of the 9th International Conference on Information Technology and Quantitative Management, 2022

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning.
IEEE Trans. Mob. Comput., 2021

Social-Aware Incentive Mechanism for Vehicular Crowdsensing by Deep Reinforcement Learning.
IEEE Trans. Intell. Transp. Syst., 2021

2020
Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning.
IEEE Trans. Mob. Comput., 2020

Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach.
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020


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