Our paper “Tuning parameter calibration for personalized prediction in medicine” at EJS is now published here.
Author Archives: LedererLab
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! 👑
Two new students
Welcome to our two new PhD students Ali and Pegah! Looking forward to working with you. ⛳
Personalized Medicine
Our paper “Tuning parameter calibration for prediction in personalized medicine” has been accepted at Electronic Journal of Statistics. Congratulations to our students Yannick and Shih-Ting, and a “thank you” to our wonderful collaborator Kristoffer.
Normalizing Flows
We have submitted a new paper “copula-based normalizing flows.” Great to work with you, Mike and Asja! ☄️
Statistics and Artificial Intelligence
Our paper “Is there a role for statistics in artificial intelligence?” has been accepted at Advances in Data Analysis and Classification.
Vanishing gradients
We have analized the vanishing-gradient problem in deep learning and potential remedies here. Congratulations, Leni! 🏄
Johannes Lederer 