Publications

2024

• 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 (IF: 10.6).
• 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 2024.

2023

• Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., … & Mustafa, M. A. (2023). A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation. arXiv preprint arXiv:2305.11391.
• Xu, P., Wang, F., Ruan, W., Zhang, C., & Huang, X. (2023, June). Sora: Scalable Black-Box Reachability Analyser on Neural Networks. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
• Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (2023). Hierarchical Distribution-Aware Testing of Deep Learning. ACM Transactions on Software Engineering and Methodology 2023.
• Cai, K., Lu, C. X., & Huang, X. (2023). Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings. IEEE Robotics and Automation Letters, 8(5), 2558-2565.
• Qi, Y., Zhao, X., & Huang, X. (2023). Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT. arXiv preprint arXiv:2304.01246.
• Qi, Y., Dong, Y., Zhao, X., & Huang, X. (2023). Stpa for learning-enabled systems: A survey and a new method. ITSC2023.
• Huang, X., Jin, G., & Ruan, W. (2023). Machine Learning Safety. Springer.
• Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. AAMAS’23.
• Wu, D., Jin, G., Yu, H., Yi, X., & Huang, X. (2023). Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff. arXiv preprint arXiv:2301.09522.
• Jin, G., Yi, X., Wu, D., Mu, R., & Huang, X. (2023). Randomized adversarial training via taylor expansion. CVPR2023.
• Huang, W., Zhao, X., Jin, G., & Huang, X. (2023). SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. ICCV2023.

2022

• Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang S., Zhao X., Schewe S. & Huang, X. (2022). Reliability Assessment and Safety Arguments for Machine Learning Components in Assuring Learning-Enabled Autonomous Systems. ACM Transactions on Embedded Computing Systems.
• Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2022). Short-term Load Forecasting with Distributed Long Short-Term Memory. 2023 IEEE ISGT North America.
• Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging formal methods and machine learning with global optimisation. In International Conference on Formal Engineering Methods (pp. 1-19). Springer, Cham.
• Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI Communications, 1-14.
• Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (2022). Hierarchical Distribution-Aware Testing of Deep Learning. arXiv preprint arXiv:2205.08589.
• Wu, D., Yi, X., & Huang, X. (2022). A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network. Frontiers in Neuroscience.
• Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems. IROS2022.
• Cai, K., Lu, C. X., & Huang, X. (2022). STUN: Self-Teaching Uncertainty Estimation for Place Recognition. IROS2022.
• Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. in AISafety Workshop at IJCAI2022.
• Jin, G., Yi, X., Huang, W., Schewe, S., & Huang, X. Enhancing Adversarial Training with Second-Order Statistics of Weights. CVPR 2022.
• Alshareef, A., Berthier, N., Schewe, S., & Huang, X. Quantifying the Importance of Latent Features in Neural Networks. In SafeAI’22 Workshop at AAAI’22.
• Jin, G., Yi, X., Yang, P., Zhang, L., Schewe, S., & Huang, X. (2022). Weight Expansion: A New Perspective on Dropout and Generalization. arXiv preprint arXiv:2201.09209.
• Jin, G., Yi, X., & Huang, X. (2022). Neuronal Correlation: a Central Concept in Neural Network. arXiv preprint arXiv:2201.09069.

2021

• Huang, W., Zhao, X. & Huang, X. (2021) Embedding and extraction of knowledge in tree ensemble classifiers. Machine Learning.
• Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., and Huang, X. (2021a). Assessing the reliability of deep learning classifiers through robustness evaluation andoperational profiles. In AISafety’21 Workshop at IJCAI’21.
• Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J. and Huang, X., 2021. Coverage Guided Testing for Recurrent Neural Networks , IEEE Tran. On Reliability.
• Zhao, X., Huang, W., Huang, X., Robu, V. and Flynn, D., 2021. BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations , UAI2021.
• Salako, K., Strigini, L. and Zhao, X., 2021. Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence, DSN’21.
• Zhao, X., Huang, W., Schewe, S., Dong, Y. and Huang, X., 2021. Detecting Operational Adversarial Examples for Reliable Deep Learning , DSN’21 (fast abstract).

2020

• Jin, G., Yi, X., Zhang, L., Zhang, L., Schewe, S. and Huang, X., 2020. How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? . Advances in Neural Information Processing Systems, 33.
• Meng, Y., Meng, W., Gao, D., Zhao, Y., Yang, X., Huang, X. and Zheng, Y., 2020, August. Regression of Instance Boundary by Aggregated CNN and GCN . In European Conference on Computer Vision (pp. 190-207). Springer, Cham.
• Sun, Y., Chockler, H., Huang, X. and Kroening, D., 2020, August. Explaining Image Classifiers using Statistical Fault Localization . In European Conference on Computer Vision (pp. 391-406). Springer, Cham.
• Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., Wu, M. and Yi, X., 2020. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability . Computer Science Review, 37, p.100270.
• Sun, Y., Zhou, Y., Maskell, S., Sharp, J. and Huang, X., 2020, May. Reliability validation of learning enabled vehicle tracking . In 2020 IEEE International Conference on Robotics and Automation (ICRA) (pp. 9390-9396). IEEE.
• Li, R., Li, J., Huang, C.C., Yang, P., Huang, X., Zhang, L., Xue, B. and Hermanns, H., 2020, November. PRODeep: a platform for robustness verification of deep neural networks . In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1630-1634).
• Meng, Y., Wei, M., Gao, D., Zhao, Y., Yang, X., Huang, X. and Zheng, Y., 2020, October. CNN-GCN aggregation enabled boundary regression for biomedical image segmentation . In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 352-362). Springer, Cham.
• Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M. and Huang, X., 2020, September. A safety framework for critical systems utilising deep neural networks . In International Conference on Computer Safety, Reliability, and Security (pp. 244-259). Springer, Cham.
• Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S. and Huang, X., 2020. Practical Verification of Neural Network Enabled State Estimation System for Robotics .

2019

• Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M. and Ashmore, R., 2019. Structural test coverage criteria for deep neural networks . ACM Transactions on Embedded Computing Systems (TECS), 18(5s), pp.1-23.
• Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., Fisher, M., Papacchini, F. and Ferrando, A., 2019, September. Towards integrating formal verification of autonomous robots with battery prognostics and health management . In International Conference on Software Engineering and Formal Methods (pp. 105-124). Springer, Cham.
• Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D. and Kwiatkowska, M., 2019, August. Global robustness evaluation of deep neural networks with provable guarantees for the hamming distance . IJCAI.
• Wu, M., Louw, T., Lahijanian, M., Ruan, W., Huang, X., Merat, N. and Kwiatkowska, M., 2019, November. Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles . In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 6210-6216). IEEE.
• Sun, Y., Huang, X., Kroening, D., Sharp, J., Hill, M. and Ashmore, R., 2019, May. DeepConcolic: Testing and debugging deep neural networks . In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 111-114). IEEE.

2018

• Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M. and Kroening, D., 2018, September. Concolic testing for deep neural networks . In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (pp. 109-119).
• Wicker, M., Huang, X. and Kwiatkowska, M., 2018, April. Feature-guided black-box safety testing of deep neural networks . In International Conference on Tools and Algorithms for the Construction and Analysis of Systems (pp. 408-426). Springer, Cham.
• Ruan, W., Huang, X. and Kwiatkowska, M., 2018. Reachability analysis of deep neural networks with provable guarantees . arXiv preprint arXiv:1805.02242.