我院杨玥含副教授团队在计算机领域顶级期刊Pattern Recognition和Expert Systems With Applications上分别发表了关于迁移学习问题与分类数据问题的研究论文。
在Pattern Recognition期刊上，杨玥含副教授与学生高旖淼共同发表了一篇关于迁移学习问题的论文。该论文提出了一种名为Joint Estimation Transferred from Strata（JETS）的方法，该方法用于在高维回归模型中进行联合估计。通过在辅助模型中利用信息，同时避免噪声影响，JETS旨在获得稳定的估计结果。此外，JETS具有计算优势，可以与传统惩罚优化方法相结合，并在生存分析、线性回归等领域得到应用。实验结果显示，JETS在各种情况下都表现出良好的性能。
论文题目：Transfer learning on stratified data: joint estimation transferred from strata
论文摘要：This paper studies the target model with the help of auxiliary models from different but possibly related groups. Inspired by transfer learning, we propose a method called joint estimation transferred from strata (JETS). To obtain a sparse solution, JETS constructs a penalized framework combining a term that penalizes the target model and an additional term that penalizes the differences between auxiliary models and the target model. In this way, JETS overcomes the challenge caused by the limited samples in high-dimensional study, and obtains stable and accurate estimates regardless of whether auxiliary samples contain noisy information. We demonstrate that this method enjoys the computational advantage of the traditional methods such as the lasso. During simulations and applications, the proposed method is compared with several existing methods and JETS outperforms others.
在Expert Systems With Applications期刊上，杨玥含副教授作为独立作者发表了一篇关于分类数据的论文。该论文主要研究了分类数据中具有两个或多个响应的情况，提出了一种一致性维度约简的方法。通过实施一致性维度约简技术，论文提出了一种高效的迭代算法，该算法在两个响应模型和多个响应模型中的性能优于其他方法。实验证明，该算法适用于真实数据集，可以显著降低预测误差和预测均方误差。此外，该方法还提供了理论保障，证明了所提出估计的唯一性和准确性。
论文题目：Dimension reduction of high-dimension categorical data with two or multiple responses considering interactions between responses
论文摘要：This paper focuses on modeling the categorical data with two or multiple responses. We study the interactions between the responses and propose an efficient iterative procedure based on sufficient dimension reduction. We show that the proposed method reaches the local and global dimension reduction efficiency. The theoretical guarantees of the method are provided under the two- and multiple-response models. We demonstrate the uniqueness of the proposed estimator, further, we prove that the iteration converges to the oracle least squares solution in the first two and q steps for the two- and multiple-response model, respectively. For
data analysis, the proposed method is efficient in the multiple-response model and performs better than some existing methods built in the multiple-response models. We apply this modeling and the proposed method to an adult dataset and a right heart catheterization dataset. Results show that both datasets are suitable for the multiple-response model and the proposed method always performs better than the compared methods.
杨玥含，中央财经大学副教授，主要从事多重结构数据建模、因果推断、迁移学习等研究，在Journal of the American Statistical Association、Biometrika、Applied Mathematical Modelling、Pattern Recognition等国内外期刊发表论文三十余篇。