Personal profile
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being -
SDG 7 Affordable and Clean Energy -
SDG 9 Industry, Innovation, and Infrastructure -
SDG 11 Sustainable Cities and Communities
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Dive into the research topics where Xuefeng Liu is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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DiffLoader: Environment-Adaptive Computation 1 Offloading via Generative Prompt-Conditional Planning
Hu, Z., Niu, J., Ren, T., Liu, X., Li, M. & Guizani, M., 2026, (Accepted/In press) In: IEEE Transactions on Mobile Computing.Research output: Contribution to journal › Article › peer-review
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Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
Zhang, R., Niu, J., Liu, X., Tang, S. & Yuan, J., 2026, Proceedings of the AAAI Conference on Artificial Intelligence. Koenig, S., Jenkins, C. & Taylor, M. E. (eds.). 43 ed. Association for the Advancement of Artificial Intelligence, p. 36509-36517 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 40, no. 43).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access -
基于扩散模型的具身智能模仿学习综述
Translated title of the contribution :Recent advances in diffusion-based embodied imitation learning: a survey Research output: Contribution to journal › Review article › peer-review
Open Access -
3DFaceSculptor: A Common Framework for Image-Guided 3D Face Deformation
Su, H., Wang, X., Niu, J., Liu, X., Wu, X. & Wang, N., 2025, In: IEEE Transactions on Visualization and Computer Graphics. 31, 12, p. 10311-10327 17 p.Research output: Contribution to journal › Article › peer-review
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Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
Guo, W., Liu, X., Wang, H., Niu, J., Tang, S. & Yuan, J., 2025, EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025. Christodoulopoulos, C., Chakraborty, T., Rose, C. & Peng, V. (eds.). Association for Computational Linguistics (ACL), p. 23986-24013 28 p. (EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review