【专家简介】：毛晓军，上海交通大学长聘教轨副教授。他的研究领域包括分布式统计推断，推荐系统和高维数据分析。主要研究成果发表于AOS, JASA, JMLR, IEEE (TIT, TSP, TIFS), ICML, NeurIPS, 《管理世界》等期刊及会议上。先后主持国家自然科学基金优秀青年基金项目、面上项目。
【报告摘要】：The emergence of big data has enabled the creation of significant models by allowing the storage of large data volumes. Transfer learning is a machine learning technique that transfers knowledge between different domains by utilizing pretrained models from the source domain to optimize the target domain. In contrast, inductive matrix completion is a method that leverages side information from multiple sources to improve task performance. This paper explores inductive matrix completion within the transfer learning framework, with our proposed approach assuming group sparsity for the difference between the core matrices of the target and source domains. Theoretical guarantees of our method are investigated to demonstrate the gains achieved through transfer learning compared with vanilla inductive matrix completion. Several synthetic experiments are conducted to evaluate the performances of the proposed approach and existing methods, demonstrating that our method outperforms others.
【报告时间】：2024年11月28日 10:00-11:00
【报告地点】：位育楼417
