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.
Author Archives: LedererLab
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! 🏄
Two new papers on deep learning
We two new papers on deep learning, one on targeted deep learning and one on robust deep learning. Well done, Shih-Ting!
Regularized neural networks
The published version of “Statistical guarantees for regularized neural networks” can now be found
here.
Paper on sparse deep learning accepted
Our paper “Statistical guarantees for regularized neural networks” has been accepted at Neural Networks. Congrats, Mahsa and Fang! 🧉
Two recent biostats papers now online
FDR control rev.
Our paper “Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data” has now been published. Congratulations again to Fang!
Paper on FDR control accepted
Our paper “Aggregated false discovery rate control” has been accepted at Entropy. Great job, Fang! 🎬
