We have developed a theoretical framework and numerical algorithms for estimating graphical models with data beyond Gaussian distributions. The paper can be found here; the software can be found here. Great work, Rui! 🤩
Author Archives: LedererLab
Oracle inequalities for high-dimensional prediction
Our paper on oracle inequalities is now published in Bernoulli 2019, Vol. 25, No. 2, 1225-1255. The weblink is here.
Team Updates
Our RUB-team has gained a number of new members over the last months. We welcome the post-doctoral researcher Fang Xie (University of Macau, China), the PhD students Yannick Düren (RTWH Aachen), Shih-Ting Huang (Temple University, USA/National Taipei University, Taiwan), and Mahsa Taheri (Esfahan University of Technology, Iran), and the Master’s student Mike Laszkiewicz (RUB)!
High-dimensional Logistic Regression With Guarantees
Our paper “Tuning parameter calibration for l1-regularized logistic regression” has been accepted at JSPI. Congratulations Wei! 🤠
Bronze Medal at Datathon
Together with her team, Rui has achieved third place at the first Citadel Datathon at UW this weekend. Congratulations!
Maximum regularized likelihood estimators
Our paper “Maximum regularized likelihood estimators: A general prediction theory and applications” has now been published here.
Prediction Theory for MRLE
Our paper “Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications” has been accepted in STAT. Congratulations, Rui! 😎
