Our grant proposal Statistical Theories for Sparse Deep Learning was selected for funding by the German Research Foundation. Thank you! We are looking forward to working on an exciting topic! 🎬
Author Archives: LedererLab
Graphical Models With the TREX
Our paper “Topology Adaptive Graph Estimation in High Dimensions” is now accepted and published in Mathematics here. Cheers, Christian! 🍺
New Paper on Variable Clustering
We have a new paper “VC-PCR: a prediction method based on supervised variable selection and clustering” about variable clustering in transcriptomics and in general. Great work especially from Rebecca, who just defended her PhD, and from Rainer and Bernadette, the two other members of our Belgian🇧🇪-American🇺🇸-German🇩🇪 tag team!
Depth-Normalization Paper Accepted
Our paper “Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method” has been accepted at Nucleic Acids Research. Congratulations, Yannick and Li-Xuan! 🎊
Depth Normalization of Small RNA Sequencing
Our paper “Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method” is now available on arXiv. Great to work with you, Yannick and Li-Xuan! 🦠
Latest EJS Paper Published
Our paper “Tuning parameter calibration for personalized prediction in medicine” at EJS is now published here.
Postdoctoral visitor
Somnath Chakraborty has secured a three-month fellowship from the RUB research school to visit us here in Bochum! Congratulations! 🥳
Latest JMLR Paper Online
Our paper “Estimating the lasso’s effective noise” at JMLR is now online here.
Fundamentals of high-dimensional statistics 📖
The textbook “Fundamentals of High-Dimensional Statistics: With Exercises and R Labs” has now appeared as a part of Springer Texts in Statistics. The book features step-by-step introductions, numerous exercises with solutions, and R labs. It is available as a hardcover, softcover, and ebook. Most university libraries have access to the ebook already—ask them otherwise, or ask them to get an actual book if you prefer. Get more information about the book here.
Lasso: tuning-parameter calibration and inference
Our paper “Estimating the lasso’s effective noise” has been accepted at JMLR. A “thank you” to our wonderful collaborator Michael! 👑
Johannes Lederer 