把Trans-Lasso算法拓展到了Functional Linear Regression的情况。Structural Interpretability是指考虑functional space 是Reproducing Kernel Hilbert Spaces(RKHS)的情况。使得在其上的量化相似度和distance是可以用RKHS自身的性质解释的。 Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression[J...
multitask learningQ‐aggregationThis paper considers estimation and prediction of a high‐dimensional linear regression in the setting of transfer learning where, in addition to observations from the target model, auxiliary samples from different but possibly related regression models are available. When ...
Zhang, Y., & Zhu, Z. (2025). Transfer learning for high-dimensional quantile regression via convolution smoothing. Statistica Sinica, 35, 1–39. High-dimensionalLinear Regression Li, S., Cai, T. T., & Li, H. (2022). Transfer learning for high-dimensional linear regression: Prediction, ...
for high-dimensional linear regression. Its theoretical guarantees and minimax optimality will be demonstrated. Next, I will introduce a transferred Q-learning algorithm, which can integrate source data into a target offline or online reinforcement learning problem. Improvement in policy learning will be...
Transfer learning for high-dimensional linear regression via the elastic net In this paper, the high-dimensional linear regression problem is explored via the Elastic Net under the transfer learning framework. Within this framework,... K Meng,Y Gai,X Wang,... - Knowledge-Based Systems 被引量:...
转http://www.zhizhihu.com/html/y2009/790.html迁移学习(Transfer Learning)TL在机器学习领域,迁移学习(Transfer learning)是一个比较新的名词。目前国内做这个方面的很少,我目前只知道香港科技大学杨强教授及上海交大的机器学习
for legal case reports. They describe their knowledge acquisition framework, which lets them quickly build classification rules, using a small number of features to assign general labels to cases. They show how the resulting knowledge base outperforms machine learning models, which use both the ...
Deep learning can “capture abstract features and recognise patterns in ways many once thought impossible for computers” (Miyajima, 2017), and DNN has “the ability to learn multiple nonlinear transformations with high complexity through multiple hidden layers, which helps to capture the main ...
Recently, machine learning has been also shown to be a useful method for source localization. For example, Huang et al. applied deep neural networks to acoustic source localization in shallow water environments6. Vera-Diaz et al. used deep learning to directly estimate the three-dimensional ...
坐车简单读了两篇paper | pic1:Profiled Transfer Learning for High Dimensional Linear Model感觉这个setting比较像是model aggregation,把aggregation的parametric作一个estimate,感觉这种setting不好找到应用、、(test我感觉可能补上会比较好)但后边的实验确实比较有意思,用了个刑法文本-量刑的数据集...还是第一次见pi...