Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
The least-absolute shrinkage and selection operator (Lasso) is a popular tool for joint estimation and continuous variable selection, especially well-suited for the under-determin...
In this paper, we present a novel semisupervised regression algorithm working on multiclass data that may lie on multiple manifolds. Unlike conventional manifold regression algori...
Huan Wang, Shuicheng Yan, Thomas S. Huang, Jianzhu...
Abstract. Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniqu...
Frank Rosenthal, Peter Benjamin Volk, Martin Hahma...