This semester, we are offering a course on high-dimensional statistics and a seminar on statistical learning. The course might be of interest to students in mathematics and well beyond (CS, physics, biology …)—feel free to check out the details on STiNE.
University of Hamburg
Our team has moved to University of Hamburg. Thank you for the warm welcome here in Hamburg. We are excited to start contributing to the UHH community! 🚢 🌊 🎇
Watermarking
Our new paper “Set-membership inference attacks using data watermarking” is now on arXiv. Nicely done, Mike, Denis, and Asja! 🚀🚀🚀
Deep generative models
We have put a new paper called “Single-model attribution via final-layer inversion” about deep generative models on arXiv. Wonderful job, Mike, Jonas, and Asja! 🥁🎷🪗🎻
High-Dimensional Extremes
Together with our amazing collaborator Marco Oesting, we have put a new paper with the title “Extremes in high dimensions: methods and scalable algorithms” on arXiv. Thanks for the great work, Marco! 🍻
Lag selection and stability in AR processes
With Somnath and our collaborator Rainer von Sachs, we have put a new paper with the title “Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming” on arXiv. Well done, Somnath! 🥇
Mahsa passed final exams
Mahsa passed her doctoral exam, earning her doctorate in Mathematics from Ruhr-University Bochum. Great job, Dr. Taheri! 🎓
Paper on Targeted Deep Learning Accepted
Shih-Ting’s paper on targeted deep learning has been accepted at Stat. Good job, Shih-Ting! ⛰️
DeepMom paper published
Shih-Ting’s paper “DeepMoM: Robust Deep Learning With Median-of-Means” is now published in volume 32 of the Journal of Computational and Graphical Statistics. Find the paper here.
Johannes Lederer 