Research outputs
2026
Beyond shortcuts: Mitigating spurious correlations in radiological diagnosis with causal intervention
Zeng, X., Wang, J., Zhang, J., Dong, Y., Wang, W., Jiang, Y., . . . Zhang, H. (2026). Beyond shortcuts: Mitigating spurious correlations in radiological diagnosis with causal intervention. KNOWLEDGE-BASED SYSTEMS, 348, 12 pages. doi:10.1016/j.knosys.2026.116375
Enhancing robustness of LLM-driven multi-agent systems through randomized smoothing
HU, J., DONG, Y., DING, Z., & HUANG, X. (2026). Enhancing robustness of LLM-driven multi-agent systems through randomized smoothing. Chinese Journal of Aeronautics, 39(7). doi:10.1016/j.cja.2025.103779
A Multimodal LLM Framework with Time-Series-Guided Visual Routing for Ultra-Short-Term Solar Forecasting
Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments
Hu, J., Dong, Y., Sun, Y., & Huang, X. (2026). Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments. In Proceedings of the Aaai Conference on Artificial Intelligence Vol. 40 (pp. 29477-29485). doi:10.1609/aaai.v40i35.40189
2025
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
Ao, S., Dong, Y., Hu, J., & Ramchurn, S. D. (2025). Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs. TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 13, 1474-1487. doi:10.1162/TACL.a.44
Feature Purification Matters: Suppressing Outlier Propagation for Training-Free Open-Vocabulary Semantic Segmentation
Jin, S., Yu, S., Zhang, B., Sun, M., Dong, Y., & Xiao, J. (2025). Feature Purification Matters: Suppressing Outlier Propagation for Training-Free Open-Vocabulary Semantic Segmentation. In 2025 IEEE/CVF International Conference on Computer Vision (ICCV) Vol. 00 (pp. 20291-20300). Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/iccv51701.2025.01887
Safeguarding large language models: a survey
Dong, Y., Mu, R., Zhang, Y., Sun, S., Zhang, T., Wu, C., . . . Huang, X. (2025). Safeguarding large language models: a survey. ARTIFICIAL INTELLIGENCE REVIEW, 58(12), 56 pages. doi:10.1007/s10462-025-11389-2
Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models
Nash equilibrium seeking in non-cooperative heterogeneous multi-robot systems via output regulation
Dong, Y., Li, Z., Ehsan, S., Ding, Z., & Huang, X. (2025). Nash equilibrium seeking in non-cooperative heterogeneous multi-robot systems via output regulation. Neurocomputing, 619, 129179. doi:10.1016/j.neucom.2024.129179
A Temporal Scale Transformer Framework for Precise Remaining Useful Life Prediction in Fuel Cells
FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model
Hu, J., Huang, Z., Yin, X., Ruan, W., Cheng, G., Dong, Y., & Huang, X. (2025). FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model. In Advances in Neural Information Processing Systems Vol. 38 (pp. 113280-113302).
Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems
Hu, J., Tang, Z., Jin, X., Zhang, B., Dong, Y., & Huang, X. (2025). Hierarchical Testing With Rabbit Optimization for Industrial Cyber-Physical Systems. IEEE TRANSACTIONS ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, 3, 472-484. doi:10.1109/TICPS.2025.3586988
Responsible and adaptive robots in care home settings: an implementation framework analysis of a workshop with public and professionals
Boudouraki, A., Waheed, M., Mestre, R., Landowska, A., Georgara, A., Deshmukh, J., . . . Bergin, A. G. (2025). Responsible and adaptive robots in care home settings: an implementation framework analysis of a workshop with public and professionals. Frontiers in Robotics and AI, 12. doi:10.3389/frobt.2025.1610329
2024
A survey of safety and trustworthiness of large language models through the lens of verification and validation
Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., . . . Mustafa, M. A. (2024). A survey of safety and trustworthiness of large language models through the lens of verification and validation. Artificial Intelligence Review, 57(7). doi:10.1007/s10462-024-10824-0
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Dong, Y., Zhao, X., Wang, S., & Huang, X. (2024). Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems. IEEE Robotics and Automation Letters, 1-8. doi:10.1109/lra.2024.3364471
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
Dong, Y., Wang, Y., Gama, M., Mustafa, M. A., Deconinck, G., & Huang, X. (2024). Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting. IEEE Internet of Things Journal, 1. doi:10.1109/jiot.2024.3362587
Position: Building Guardrails for Large Language Models Requires Systematic Design
Dong, Y., Mu, R., Jin, G., Qi, Y., Hu, J., Zhao, X., . . . Huang, X. (2024). Position: Building Guardrails for Large Language Models Requires Systematic Design. In Proceedings of Machine Learning Research Vol. 235 (pp. 11375-11394).
2023
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2023). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 22(3), 48 pages. doi:10.1145/3570918
Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2023). Short-term Load Forecasting with Distributed Long Short-Term Memory. In 2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT. doi:10.1109/ISGT51731.2023.10066368
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 2023-May (pp. 1421-1429).
STPA for Learning-Enabled Systems: A Survey and A New Practice
Qi, Y., Dong, Y., Khastgir, S., Jennings, P., Zhao, X., & Huang, X. (2023). STPA for Learning-Enabled Systems: A Survey and A New Practice. In 2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC (pp. 1381-1388). doi:10.1109/ITSC57777.2023.10422520
2022
Short-term Load Forecasting with Distributed Long Short-Term Memory
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2022). Short-term Load Forecasting with Distributed Long Short-Term Memory. Retrieved from https://arxiv.org/abs/2208.01147v2
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2021). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. Retrieved from https://doi.org/10.1109/IROS47612.2022.9981794
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 5171-5178). doi:10.1109/IROS47612.2022.9981794
EnnCore: End-to-End Conceptual Guarding of Neural Architectures
Manino, E., Carvalho, D., Dong, Y., Rozanova, J., Song, X., Mustafa, M. A., . . . Cordeiro, L. (2022). EnnCore: End-to-End Conceptual Guarding of Neural Architectures. In Ceur Workshop Proceedings Vol. 3087.
2021
Short term load forecasting with markovian switching distributed deep belief networks
Dong, Y., Dong, Z., Zhao, T., Li, Z., & Ding, Z. (2021). Short term load forecasting with markovian switching distributed deep belief networks. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 130, 9 pages. doi:10.1016/j.ijepes.2021.106942
A Strategic Day-ahead bidding strategy and operation for battery energy storage system by reinforcement learning
Dong, Y., Dong, Z., Zhao, T., & Ding, Z. (2021). A Strategic Day-ahead bidding strategy and operation for battery energy storage system by reinforcement learning. ELECTRIC POWER SYSTEMS RESEARCH, 196, 14 pages. doi:10.1016/j.epsr.2021.107229
Detecting Operational Adversarial Examples for Reliable Deep Learning
Zhao, X., Huang, W., Schewe, S., Dong, Y., & Huang, X. (2021). Detecting Operational Adversarial Examples for Reliable Deep Learning. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS - SUPPLEMENTAL VOL (DSN 2021) (pp. 5-6). doi:10.1109/DSN-S52858.2021.00013
Detecting Operational Adversarial Examples for Reliable Deep Learning
Fully-Distributed Deloading Operation of DFIG-Based Wind Farm for Load Sharing
Dong, Z., Li, Z., Dong, Y., Jiang, S., & Ding, Z. (2021). Fully-Distributed Deloading Operation of DFIG-Based Wind Farm for Load Sharing. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 12(1), 430-440. doi:10.1109/TSTE.2020.3002690
2020
An Optimal Day-ahead Bidding Strategy and Operation for Battery Energy Storage System by Reinforcement Learning
Dong, Y., Zhao, T., & Ding, Z. (2020). An Optimal Day-ahead Bidding Strategy and Operation for Battery Energy Storage System by Reinforcement Learning. In IFAC PAPERSONLINE Vol. 53 (pp. 13190-13195). doi:10.1016/j.ifacol.2020.12.144
2019
Demand-side management using a distributed initialisation-free optimisation in a smart grid
Dong, Y., Zhao, T., & Ding, Z. (2019). Demand-side management using a distributed initialisation-free optimisation in a smart grid. IET RENEWABLE POWER GENERATION, 13(9), 1533-1543. doi:10.1049/iet-rpg.2018.5858