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Research

My research interests have focused on two major areas. The first is statistical machine learning and data mining, where I have concentrated on developing scalable tools for analyzing massive data with complex structures; in particular, efficient techniques for independent and time dependent data models, and more flexible statistical methods for analyzing both static and dynamic networks. My second research area is on generative AI, where I have worked on topics including on-device LLM, language-guided image generation, text-to-3D synthesis, and image editing.

Selected Articles (Full list see  google scholar references)

  • R. Wang, Z. Chen, C. Chen, J. Ma, H. Lu, and X. Lin, Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2024).
  • F. Shi, P. Qing, D. Yang, N. Wang, Y. Lei, H. Lu, X. Lin, and D. Li. Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models. In Findings of the Association for Computational Linguistics (NAACL 2024).
  • Y. Lei, Z. Xue, X. Zhao, H. Sun, S. Zhu, X. Lin and D. Xiong, CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023).
  • H. Zhang, Y. Sun, W. Guo, Y. Liu, H. Lu, X. Lin and H. Xiong, Interactive Interior Design Recommendation via Coarse-to-fine Multimodal Reinforcement Learning. In Proceedings of the 31st ACM International Conference on Multimedia (ACM MM 2023).
  • W. Wu, J. Wang, Y. Zhang, Z. Liu, L. Zhou and X. Lin (2023), VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear Computations. IEEE Transactions on Information Forensics & Security. 18, 4214-4228.
  • D. Yang, P. Qing, Y. Li, H. Lu and X Lin, GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), 745-760.
  • D. Zhang, Z. Yuan, H. Liu, X. Lin, and H. Xiong, Learning to Walk with Dual Agents for Knowledge Graph Reasoning. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022).
  • M. Pham, Y. Du, X. Lin, and A. Ruszczynski (2021), An Outer-inner Linearization Method for Non-convex and Nondifferentiable Composite Regularization Problems, Journal of Global Optimization. 81 (1), 179-202.
  • Y. Du, X. Lin, M. Pham, and A. Ruszczynski (2021), Selective Linearization for Multi-block Statistical Learning, European Journal of Operational Research. 293 (1), 219-228.
  • R. Zhou, Q. Zhang, P. Zhang, L. Niu and X. Lin (2021), Anomaly Detection in Dynamic Attributed Networks. Neural Computing and Applications. 33 (6), 2125-2136.
  • M. Alaziz, Z. Jia, R. Howard, X. Lin, and Y. Zhang (2020), In-Bed Body Motion Detection and Classification System. ACM Transactions on Sensor Networks. 16(2), 1-26.
  • R. Zhou, Q. Zhang, P. Zhang, L. Niu and X. Lin (2020), Anomaly Detection in Dynamic Attributed Networks. Neural Computing and Applications. In press.
  • Y. Du, X. Lin, and A. Ruszczynski (2017), Selective Linearization For Multi-Block Convex Optimization. SIAM Journal on Optimization. 27(2),1102-1117.
  • X. Lin, M. Pham, and A. Ruszczynski (2014), Alternating Linearization for Structured Regularization Problems. Journal of Machine Learning Research. 15(Oct), 3447-3481.
  • Airoldi, X. Wang, and X. Lin (2013), Multi-way Blockmodels for Analyzing Coordinated High-dimensional Responses. Annals of Applied Statistics. 7(4), 2431-2457.
  • J. Ding, X. Xie and X. Lin (2012), A Simple Provably Secure Key Exchange Scheme Based on the Learning with Errors Problem,  Cryptology ePrint Archive, 688.
  • Y. Sun and X. Lin (2012), Regularization for Stationary Multivariate Time Series. Quantitative Finance. 12(4), 573-586.
  • X. Zhu, P. Zhang, X. Lin, and Y. Shi (2010), Active Learning from Stream Data with Optimal Weight Classifier Ensemble. IEEE Transactions on SMC-Part B. 40(6), 1607-1621.
  • X. Lin, J. Pittman and B. Clarke (2007), Information Conversion, Effective Samples, and Parameter Size. IEEE Transactions on Information Theory. 53(12), 4438-4456.
  • A. Karr, X. Lin, A. P. Sanil, and J. P. Reiter (2005), Secure Regression on Distributed Databases. Journal of Graphical and Computational Statistics. 14, 263 – 279.