{"id":365,"date":"2020-07-09T16:56:57","date_gmt":"2020-07-09T16:56:57","guid":{"rendered":"http:\/\/sites.rutgers.edu\/xiaodong-lin\/?page_id=365"},"modified":"2024-07-16T02:34:23","modified_gmt":"2024-07-16T02:34:23","slug":"research","status":"publish","type":"page","link":"https:\/\/sites.rutgers.edu\/xiaodong-lin\/research\/","title":{"rendered":"Research"},"content":{"rendered":"<p style=\"text-align: left\">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.<\/p>\n<hr \/>\n<p><b>Selected Articles <\/b>(Full list see \u00a0<a href=\"https:\/\/scholar.google.com\/citations?user=c9dbPzgAAAAJ&amp;hl=en\">google scholar references<\/a>)<\/p>\n<ul>\n<li>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).<\/li>\n<li>F. Shi, P. Qing, D. Yang, N. Wang, Y. Lei, H. Lu, X. Lin, and D. Li. <a href=\"https:\/\/aclanthology.org\/2024.findings-naacl.119\">Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models<\/a>. In <i>Findings of the Association for Computational Linguistics<\/i>\u00a0(NAACL<i> 2024<\/i>).<\/li>\n<li>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).<\/li>\n<li>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).<\/li>\n<li>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 &amp; Security. 18, 4214-4228.<\/li>\n<li>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.<\/li>\n<li>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).<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>M. Alaziz, Z. Jia, R. Howard, X. Lin, and Y. Zhang (2020), <a href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3372023\">In-Bed Body Motion Detection and Classification System<\/a>. <em>ACM Transactions on Sensor Networks<\/em>. 16(2), 1-26.<\/li>\n<li>R. Zhou, Q. Zhang, P. Zhang, L. Niu and X. Lin (2020), <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05091-3\">Anomaly Detection in Dynamic Attributed Networks<\/a>. <em>Neural Computing and Applications<\/em>. In press.<\/li>\n<li>Y. Du, X. Lin, and A. Ruszczynski (2017), <a href=\"https:\/\/epubs.siam.org\/doi\/abs\/10.1137\/15M103217X\">Selective Linearization For Multi-Block Convex Optimization<\/a>. <em>SIAM Journal on Optimization<\/em>. 27(2),1102-1117.<\/li>\n<li><span style=\"font-size: 1rem\">X. Lin, M. Pham, and A. Ruszczynski (2014), <a href=\"http:\/\/www.jmlr.org\/papers\/volume15\/lin14a\/lin14a.pdf\">Alternating Linearization for Structured Regularization Problems<\/a>. <em>Journal of Machine Learning Research<\/em>. 15(Oct), 3447-3481.<\/span><\/li>\n<li><span style=\"font-size: 1rem\">Airoldi, X. Wang, and X. Lin (2013), <a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3935422\/\">Multi-way Blockmodels for Analyzing Coordinated High-dimensional Responses<\/a>. <em>Annals of Applied Statistics<\/em>. 7(4), 2431-2457.<\/span><\/li>\n<li>J. Ding, X. Xie and X. Lin (2012), <a href=\"http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.638.6793&amp;rep=rep1&amp;type=pdf\">A Simple Provably Secure Key Exchange Scheme Based on the Learning with Errors Problem<\/a>, \u00a0<em>Cryptology ePrint Archive<\/em>, 688.<\/li>\n<li>Y. Sun and X. Lin (2012), <a href=\"https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/14697688.2012.664933\">Regularization for Stationary Multivariate Time Series<\/a>. <em>Quantitative Finance<\/em>. 12(4), 573-586.<\/li>\n<li>X. Zhu, P. Zhang, X. Lin, and Y. Shi (2010), <a href=\"http:\/\/www.cse.fau.edu\/~xqzhu\/papers\/TC.Zhu.2010.Stream.pdf\">Active Learning from Stream Data with Optimal Weight Classifier Ensemble<\/a>. <em>IEEE Transactions on SMC-Part B<\/em>. 40(6), 1607-1621.<\/li>\n<li style=\"text-align: left\">X. Lin, J. Pittman and B. Clarke (2007), <a href=\"https:\/\/statistics.unl.edu\/faculty\/cv\/LinPitmanClarke2007.pdf\">Information Conversion, Effective Samples, and Parameter Size<\/a>.<em> IEEE Transactions on<\/em>\u00a0<em>Information Theory<\/em>. 53(12), 4438-4456.<\/li>\n<li>A. Karr, X. Lin, A. P. Sanil, and J. P. Reiter (2005), <a href=\"http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.129.6122&amp;rep=rep1&amp;type=pdf\">Secure\u00a0Regression on Distributed Databases<\/a>. <em>Journal of Graphical and<\/em>\u00a0<em>Computational Statistics<\/em>. 14, 263 &#8211; 279.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>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 &hellip; <a href=\"https:\/\/sites.rutgers.edu\/xiaodong-lin\/research\/\" class=\"\">Read More<\/a><\/p>\n","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-custom.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-365","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Research - Xiaodong Lin<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sites.rutgers.edu\/xiaodong-lin\/research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research - Xiaodong Lin\" \/>\n<meta property=\"og:description\" content=\"My research interests have focused on two major areas. 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