Our new paper “Benchmarking the fairness of image upsampling methods” is now on arXiv. Awesome job, Mike—and thanks Imant, Julia, and Asja for the great collaboration! 🇩🇪🇨🇭🏴☠️
Category Archives: New Paper
Change points in time series
Our discussion paper in JRSSB about “Probabilistic and Statistical Aspects of Machine Learning” is available online. Congratulations, Ayla! 🇬🇧
Affine Invariance in Neural Networks
Our new paper “Affine invariance in continuous-domain convolutional neural networks” is now on arXiv. Well done, Ali! 🖥
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! 🥇
Two new papers on deep learning
New Paper in AStA Adv. Stat. Anal.
We have a new paper with the title “Statistical guarantees for sparse deep learning” in AStA Adv. Stat. Anal.