報告時間：2023年12月21日（星期四）10:00-11:00
報告地點：翡翠湖校區科教樓B座1710室
報 告 人：林路 教授
工作單位：山東大學
舉辦單位：數學學院
報告簡介：
A basic condition for efficient transfer learning is the similarity between the target model and source models. In practice, however, the similarity condition is difficult to meet or is even violated. Instead of the similarity condition, a brand-new strategy, linear correlation ratio, is introduced in this paper to build an accurate relationship between the models. Such a correlation ratio can be easily estimated by historical data or historical characteristics of permanent variables. Then, an accurate transfer learning likelihood is established based on the correlation ratio combination. On the practical side, the new framework is applied to some application scenarios, specially the area of data streams. Methodologically, some techniques are suggested for transferring the information from simple source models to a relatively complex target model. Theoretically, some favorable properties, including the global convergence rate, are achieved, even for the case where the source models are not similar to the target model. All in all, it can be seen from the theories and experimental results that the inference on the target model is significantly improved by the information from similar or dissimilar source models, and it is somewhat surprising that a related phenomenon of Stein's paradox is illustrated.
報告人簡介：
林路，山東大學中泰證券金融研究院教授、博士生導師，第一和第二屆教育部應用統計專業碩士教育指導委員會成員，山東省教育廳應用統計專業碩土教育指導委員會成員，山東省政府參事金融法制組召集人，濟南市應用數學高等研究院院長。從事大數據、高維統計、非參數和半參數統計以及金融統計等方面的研究，在國際統計學、機器學習和相關應用學科的頂級期刊 (AOS, JMLR,《中國科學》等)和其它重要期刊發表論文120余篇，多個金融策略資政報告得到主管省長的正面批示；主持過多項國家自然科學基金、全國統計重大研發課題、教育部博士點專項基金課題、教育部新文科課題和山東省自然科學基金重點項目等，獲全國統計優秀研究成果一等獎和二等獎、山東省優秀教學成果一等獎。
