报告题目：A Correlation-Ratio Transfer Learning and Variational Stein's Paradox

报告时间：2023-05-08  14:30-16:00

报告人：林路 教授  山东大学

腾讯会议ID：231-276-960

Abstract：A basic condition for efficient transfer learning is the similarity between a 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 is estimable by historical data of a part of variables. Then a correlation-ratio transfer learning likelihood is established based on the correlation-ratio combination. On the practical side, the new framework is applied to some application scenarios, especially the areas of data streams and medical studies. Methodologically, some techniques are suggested for transferring the information from simple source models to a relatively complex target model. Theoretically, some favorable properties, including variance reduction, 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 about the target model is significantly improved by the information from similar or dissimilar source models. In other words, a variational Stein's paradox is illustrated in the context of transfer learning.