Our paper “DeepMoM: Robust Deep Learning With Median-of-Means” has been accepted at the Journal of Computational and Graphical Statistics. Awesome job, Shih-Ting! 🕺🕺

Our paper “Copula-Based Normalizing Flows” has been accepted at ICML. Cheers, Mike and Asja! 🥂

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! 🎊

Our paper “Estimating the lasso’s effective noise” has been accepted at JMLR. A “thank you” to our wonderful collaborator Michael! 👑

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.

Our paper “Is there a role for statistics in artificial intelligence?” has been accepted at Advances in Data Analysis and Classification.

Our paper “Statistical guarantees for regularized neural networks” has been accepted at Neural Networks. Congrats, Mahsa and Fang! 🧉

Our paper “Aggregated false discovery rate control” has been accepted at Entropy. Great job, Fang! 🎬

Our paper “Integrating additional knowledge into the estimation of graphical models” is now accepted at the International Journal of Biostatistics. Congratulations, Yunqi! ⛄

We got two papers accepted at this year’s AISTATS conference: “False Discovery Rates in Biological Networks” with Lu Yu and Tobias Kaufmann and “Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery” with Mike Laszkiewicz and Asja Fischer. Congratulations especially to the PhD students Lu (Toronto) and Mike (Bochum)! 🥳